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Sample records for objective particle swarm

  1. A new multi-objective particle swarm optimizer using empirical movement and diversified search strategies

    NASA Astrophysics Data System (ADS)

    Fan, Shu-Kai S.; Chang, Ju-Ming; Chuang, Yu-Chiang

    2015-06-01

    Most real-world optimization problems involve the optimization task of more than a single objective function and, therefore, require a great amount of computational effort as the solution procedure is designed to anchor multiple compromised optimal solutions. Abundant multi-objective evolutionary algorithms (MOEAs) for multi-objective optimization have appeared in the literature over the past two decades. In this article, a new proposal by means of particle swarm optimization is addressed for solving multi-objective optimization problems. The proposed algorithm is constructed based on the concept of Pareto dominance, taking both the diversified search and empirical movement strategies into account. The proposed particle swarm MOEA with these two strategies is thus dubbed the empirical-movement diversified-search multi-objective particle swarm optimizer (EMDS-MOPSO). Its performance is assessed in terms of a suite of standard benchmark functions taken from the literature and compared to other four state-of-the-art MOEAs. The computational results demonstrate that the proposed algorithm shows great promise in solving multi-objective optimization problems.

  2. Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing

    PubMed Central

    Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud

    2015-01-01

    This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets. PMID:26132309

  3. Particle Swarm Optimization Toolbox

    NASA Technical Reports Server (NTRS)

    Grant, Michael J.

    2010-01-01

    The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry

  4. Application of particle swarm optimization for improving the identification of image objects

    NASA Astrophysics Data System (ADS)

    Chiu, Nan-Hsing; Pu, Chang-En; Lin, Pei-Da; Wang, Shu-Shian

    2012-04-01

    Flight safety is very important issue for aviation industries. Analyzing the flight accidents on the basis of 2-dimensional image is hardly to illustrate the complex injuries of passengers in the flight cabin. However, how to illustrate the flight accident is a challenge from 2-dimensional space to 3-dimensional space. This study proposes a particle swarm optimization approach for improving the identification of objects from 2-dimensional image. The recognition results provide the information for building 3-dimensional systems for flight accident investigators. The experiments also show that it is a feasible approach for improving the identification of image objects.

  5. Multi-Objective Bidding Strategy for Genco Using Non-Dominated Sorting Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Saksinchai, Apinat; Boonchuay, Chanwit; Ongsakul, Weerakorn

    2010-06-01

    This paper proposes a multi-objective bidding strategy for a generation company (GenCo) in uniform price spot market using non-dominated sorting particle swarm optimization (NSPSO). Instead of using a tradeoff technique, NSPSO is introduced to solve the multi-objective strategic bidding problem considering expected profit maximization and risk (profit variation) minimization. Monte Carlo simulation is employed to simulate rivals' bidding behavior. Test results indicate that the proposed approach can provide the efficient non-dominated solution front effectively. In addition, it can be used as a decision making tool for a GenCo compromising between expected profit and price risk in spot market.

  6. Particle Swarm Optimization

    NASA Technical Reports Server (NTRS)

    Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw

    2002-01-01

    The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.

  7. Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

    PubMed

    Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

    2016-08-15

    Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. PMID:27110990

  8. Particle swarm optimized multi-objective histogram equalization for image enhancement

    NASA Astrophysics Data System (ADS)

    Shanmugavadivu, P.; Balasubramanian, K.

    2014-04-01

    Histogram Equalization (HE) is a simple and effective technique for enhancing the contrast of the input image. However, it fails to preserve the brightness while enhancing the contrast due to the abrupt mean shift during the process of equalization. Many HE based methods have been developed to overcome the problem of mean shift. But, they suffered from over-enhancement. In this paper, a multi-objective HE model has been proposed in order to enhance the contrast as well as to preserve the brightness. The central idea of this technique is to first segment the histogram of the input image into two using Otsu's threshold. A set of optimized weighing constraints are formulated and applied on both the sub-images. Then, the sub-images are equalized independently and their union produces the contrast enhanced, brightness preserved output image. Here, Particle Swarm Optimization (PSO) is employed to find the optimal constraints. This technique is proved to have an edge over the other contemporary methods in terms of entropy and contrast improvement index.

  9. Multi-objective two-stage multiprocessor flow shop scheduling - a subgroup particle swarm optimisation approach

    NASA Astrophysics Data System (ADS)

    Huang, Rong-Hwa; Yang, Chang-Lin; Hsu, Chun-Ting

    2015-12-01

    Flow shop production system - compared to other economically important production systems - is popular in real manufacturing environments. This study focuses on the flow shop with multiprocessor scheduling problem (FSMP), and develops an improved particle swarm optimisation heuristic to solve it. Additionally, this study designs an integer programming model to perform effectiveness and robustness testing on the proposed heuristic. Experimental results demonstrate a 10% to 50% improvement in the effectiveness of the proposed heuristic in small-scale problem tests, and a 10% to 40% improvement in the robustness of the heuristic in large-scale problem tests, indicating extremely satisfactory performance.

  10. Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

    PubMed

    Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

    2016-01-01

    In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

  11. A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem

    NASA Astrophysics Data System (ADS)

    Rahimi-Vahed, A. R.; Mirghorbani, S. M.; Rabbani, M.

    2007-12-01

    Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithms in large-sized problems.

  12. A new optimization algorithm based on a combination of particle swarm optimization, convergence and divergence operators for single-objective and multi-objective problems

    NASA Astrophysics Data System (ADS)

    Mahmoodabadi, M. J.; Bagheri, A.; Nariman-zadeh, N.; Jamali, A.

    2012-10-01

    Particle swarm optimization (PSO) is a randomized and population-based optimization method that was inspired by the flocking behaviour of birds and human social interactions. In this work, multi-objective PSO is modified in two stages. In the first stage, PSO is combined with convergence and divergence operators. Here, this method is named CDPSO. In the second stage, to produce a set of Pareto optimal solutions which has good convergence, diversity and distribution, two mechanisms are used. In the first mechanism, a new leader selection method is defined, which uses the periodic iteration and the concept of the particle's neighbour number. This method is named periodic multi-objective algorithm. In the second mechanism, an adaptive elimination method is employed to limit the number of non-dominated solutions in the archive, which has influences on computational time, convergence and diversity of solution. Single-objective results show that CDPSO performs very well on the complex test functions in terms of solution accuracy and convergence speed. Furthermore, some benchmark functions are used to evaluate the performance of periodic multi-objective CDPSO. This analysis demonstrates that the proposed algorithm operates better in three metrics through comparison with three well-known elitist multi-objective evolutionary algorithms. Finally, the algorithm is used for Pareto optimal design of a two-degree of freedom vehicle vibration model. The conflicting objective functions are sprung mass acceleration and relative displacement between sprung mass and tyre. The feasibility and efficiency of periodic multi-objective CDPSO are assessed in comparison with multi-objective modified NSGAII.

  13. Improving Vector Evaluated Particle Swarm Optimisation by Incorporating Nondominated Solutions

    PubMed Central

    Lim, Kian Sheng; Ibrahim, Zuwairie; Buyamin, Salinda; Ahmad, Anita; Naim, Faradila; Ghazali, Kamarul Hawari; Mokhtar, Norrima

    2013-01-01

    The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at the objective function optimised by that swarm, yielding poor solutions for the multiobjective optimisation problems. Thus, an improved Vector Evaluated Particle Swarm Optimisation algorithm is introduced by incorporating the nondominated solutions as the guidance for a swarm rather than using the best solution from another swarm. In this paper, the performance of improved Vector Evaluated Particle Swarm Optimisation algorithm is investigated using performance measures such as the number of nondominated solutions found, the generational distance, the spread, and the hypervolume. The results suggest that the improved Vector Evaluated Particle Swarm Optimisation algorithm has impressive performance compared with the conventional Vector Evaluated Particle Swarm Optimisation algorithm. PMID:23737718

  14. Particle Swarm Optimization with Double Learning Patterns

    PubMed Central

    Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

    2016-01-01

    Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747

  15. Particle Swarm Optimization with Double Learning Patterns.

    PubMed

    Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian

    2016-01-01

    Particle Swarm Optimization (PSO) is an effective tool in solving optimization problems. However, PSO usually suffers from the premature convergence due to the quick losing of the swarm diversity. In this paper, we first analyze the motion behavior of the swarm based on the probability characteristic of learning parameters. Then a PSO with double learning patterns (PSO-DLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a trade-off between the convergence speed and the swarm diversity. The particles in the master swarm and the slave swarm are encouraged to explore search for keeping the swarm diversity and to learn from the global best particle for refining a promising solution, respectively. When the evolutionary states of two swarms interact, an interaction mechanism is enabled. This mechanism can help the slave swarm in jumping out of the local optima and improve the convergence precision of the master swarm. The proposed PSO-DLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSO-DLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747

  16. Particle Swarm Transport in Fracture Networks

    NASA Astrophysics Data System (ADS)

    Pyrak-Nolte, L. J.; Mackin, T.; Boomsma, E.

    2012-12-01

    Colloidal particles of many types occur in fractures in the subsurface as a result of both natural and industrial processes (e.g., environmental influences, synthetic nano- & micro-particles from consumer products, chemical and mechanical erosion of geologic material, proppants used in gas and oil extraction, etc.). The degree of localization and speed of transport of such particles depends on the transport mechanisms, the chemical and physical properties of the particles and the surrounding rock, and the flow path geometry through the fracture. In this study, we investigated the transport of particle swarms through artificial fracture networks. A synthetic fracture network was created using an Objet Eden 350V 3D printer to build a network of fractures. Each fracture in the network had a rectangular cross-sectional area with a constant depth of 7 mm but with widths that ranged from 2 mm to 11 mm. The overall dimensions of the network were 132 mm by 166 mm. The fracture network had 7 ports that were used either as the inlet or outlet for fluid flow through the sample or for introducing a particle swarm. Water flow rates through the fracture were controlled with a syringe pump, and ranged from zero flow to 6 ml/min. Swarms were composed of a dilute suspension (2% by mass) of 3 μm fluorescent polystyrene beads in water. Swarms with volumes of 5, 10, 20, 30 and 60 μl were used and delivered into the network using a second syringe pump. The swarm behavior was imaged using an optical fluorescent imaging system illuminated by green (525 nm) LED arrays and captured by a CCD camera. For fracture networks with quiescent fluids, particle swarms fell under gravity and remained localized within the network. Large swarms (30-60 μl) were observed to bifurcate at shallower depths resulting in a broader dispersal of the particles than for smaller swarm volumes. For all swarm volumes studied, particle swarms tended to bifurcate at the intersection between fractures. These

  17. Transport of Particle Swarms Through Fractures

    NASA Astrophysics Data System (ADS)

    Boomsma, E.; Pyrak-Nolte, L. J.

    2011-12-01

    The transport of engineered micro- and nano-scale particles through fractured rock is often assumed to occur as dispersions or emulsions. Another potential transport mechanism is the release of particle swarms from natural or industrial processes where small liquid drops, containing thousands to millions of colloidal-size particles, are released over time from seepage or leaks. Swarms have higher velocities than any individual colloid because the interactions among the particles maintain the cohesiveness of the swarm as it falls under gravity. Thus particle swarms give rise to the possibility that engineered particles may be transported farther and faster in fractures than predicted by traditional dispersion models. In this study, the effect of fractures on colloidal swarm cohesiveness and evolution was studied as a swarm falls under gravity and interacts with fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with either (1) a uniform aperture or (2) a converging aperture followed by a uniform aperture (funnel-shaped). The samples consisted of two blocks that measured 100 x 100 x 50 mm. The separation between these blocks determined the aperture (0.5 mm to 50 mm). During experiments, a fracture was fully submerged in water and swarms were released into it. The swarms consisted of dilute suspensions of either 25 micron soda-lime glass beads (2% by mass) or 3 micron polystyrene fluorescent beads (1% by mass) with an initial volume of 5μL. The swarms were illuminated with a green (525 nm) LED array and imaged optically with a CCD camera. In the uniform aperture fracture, the speed of the swarm prior to bifurcation increased with aperture up to a maximum at a fracture width of approximately 10 mm. For apertures greater than ~15 mm, the velocity was essentially constant with fracture width (but less than at 10 mm). This peak suggests that two competing mechanisms affect swarm velocity in fractures. The wall provides both drag, which

  18. Selectively-informed particle swarm optimization.

    PubMed

    Gao, Yang; Du, Wenbo; Yan, Gang

    2015-01-01

    Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors. PMID:25787315

  19. Selectively-informed particle swarm optimization

    PubMed Central

    Gao, Yang; Du, Wenbo; Yan, Gang

    2015-01-01

    Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors. PMID:25787315

  20. Selectively-informed particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Gao, Yang; Du, Wenbo; Yan, Gang

    2015-03-01

    Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.

  1. Scenario-Based Multi-Objective Optimum Allocation Model for Earthquake Emergency Shelters Using a Modified Particle Swarm Optimization Algorithm: A Case Study in Chaoyang District, Beijing, China

    PubMed Central

    Zhao, Xiujuan; Xu, Wei; Ma, Yunjia; Hu, Fuyu

    2015-01-01

    The correct location of earthquake emergency shelters and their allocation to residents can effectively reduce the number of casualties by providing safe havens and efficient evacuation routes during the chaotic period of the unfolding disaster. However, diverse and strict constraints and the discrete feasible domain of the required models make the problem of shelter location and allocation more difficult. A number of models have been developed to solve this problem, but there are still large differences between the models and the actual situation because the characteristics of the evacuees and the construction costs of the shelters have been excessively simplified. We report here the development of a multi-objective model for the allocation of residents to earthquake shelters by considering these factors using the Chaoyang district, Beijing, China as a case study. The two objectives of this model were to minimize the total weighted evacuation time from residential areas to a specified shelter and to minimize the total area of all the shelters. The two constraints were the shelter capacity and the service radius. Three scenarios were considered to estimate the number of people who would need to be evacuated. The particle swarm optimization algorithm was first modified by applying the von Neumann structure in former loops and global structure in later loops, and then used to solve this problem. The results show that increasing the shelter area can result in a large decrease in the total weighted evacuation time from scheme 1 to scheme 9 in scenario A, from scheme 1 to scheme 9 in scenario B, from scheme 1 to scheme 19 in scenario C. If the funding were not a limitation, then the final schemes of each scenario are the best solutions, otherwise the earlier schemes are more reasonable. The modified model proved to be useful for the optimization of shelter allocation, and the result can be used as a scientific reference for planning shelters in the Chaoyang district

  2. Land use zoning at the county level based on a multi-objective particle swarm optimization algorithm: a case study from Yicheng, China.

    PubMed

    Liu, Yaolin; Wang, Hua; Ji, Yingli; Liu, Zhongqiu; Zhao, Xiang

    2012-08-01

    Comprehensive land-use planning (CLUP) at the county level in China must include land-use zoning. This is specifically stipulated by the China Land Management Law and aims to achieve strict control on the usages of land. The land-use zoning problem is treated as a multi-objective optimization problem (MOOP) in this article, which is different from the traditional treatment. A particle swarm optimization (PSO) based model is applied to the problem and is developed to maximize the attribute differences between land-use zones, the spatial compactness, the degree of spatial harmony and the ecological benefits of the land-use zones. This is subject to some constraints such as: the quantity limitations for varying land-use zones, regulations assigning land units to a certain land-use zone, and the stipulation of a minimum parcel area in a land-use zoning map. In addition, a crossover and mutation operator from a genetic algorithm is adopted to avoid the prematurity of PSO. The results obtained for Yicheng, a county in central China, using different objective weighting schemes, are compared and suggest that: (1) the fundamental demand for attribute difference between land-use zones leads to a mass of fragmentary land-use zones; (2) the spatial pattern of land-use zones is remarkably optimized when a weight is given to the sub-objectives of spatial compactness and the degree of spatial harmony, simultaneously, with a reduction of attribute difference between land-use zones; (3) when a weight is given to the sub-objective of ecological benefits of the land-use zones, the ecological benefits get a slight increase also at the expense of a reduction in attribute difference between land-use zones; (4) the pursuit of spatial harmony or spatial compactness may have a negative effect on each other; (5) an increase in the ecological benefits may improve the spatial compactness and spatial harmony of the land-use zones; (6) adjusting the weights assigned to each sub-objective can

  3. Land Use Zoning at the County Level Based on a Multi-Objective Particle Swarm Optimization Algorithm: A Case Study from Yicheng, China

    PubMed Central

    Liu, Yaolin; Wang, Hua; Ji, Yingli; Liu, Zhongqiu; Zhao, Xiang

    2012-01-01

    Comprehensive land-use planning (CLUP) at the county level in China must include land-use zoning. This is specifically stipulated by the China Land Management Law and aims to achieve strict control on the usages of land. The land-use zoning problem is treated as a multi-objective optimization problem (MOOP) in this article, which is different from the traditional treatment. A particle swarm optimization (PSO) based model is applied to the problem and is developed to maximize the attribute differences between land-use zones, the spatial compactness, the degree of spatial harmony and the ecological benefits of the land-use zones. This is subject to some constraints such as: the quantity limitations for varying land-use zones, regulations assigning land units to a certain land-use zone, and the stipulation of a minimum parcel area in a land-use zoning map. In addition, a crossover and mutation operator from a genetic algorithm is adopted to avoid the prematurity of PSO. The results obtained for Yicheng, a county in central China, using different objective weighting schemes, are compared and suggest that: (1) the fundamental demand for attribute difference between land-use zones leads to a mass of fragmentary land-use zones; (2) the spatial pattern of land-use zones is remarkably optimized when a weight is given to the sub-objectives of spatial compactness and the degree of spatial harmony, simultaneously, with a reduction of attribute difference between land-use zones; (3) when a weight is given to the sub-objective of ecological benefits of the land-use zones, the ecological benefits get a slight increase also at the expense of a reduction in attribute difference between land-use zones; (4) the pursuit of spatial harmony or spatial compactness may have a negative effect on each other; (5) an increase in the ecological benefits may improve the spatial compactness and spatial harmony of the land-use zones; (6) adjusting the weights assigned to each sub-objective can

  4. Modeling the Collective Strategic Searching of Artificial Insurgent Groups: A Particle Swarm Approach

    SciTech Connect

    Cui, Xiaohui; Potok, Thomas E

    2007-01-01

    A swarm based social adaptive model is proposed to model multiple insurgent groups?strategy searching in a dynamic changed environment. This report presents a pilot study on using the particle swarm modeling, a widely used non-linear optimal tool, to model the emergence of insurgency campaign. The objective of this research is to apply the particle swarm metaphor as a model of insurgent social adaptation for the dynamic environment and to provide insight and understanding of insurgent group strategic adaptation.

  5. Incremental social learning in particle swarms.

    PubMed

    de Oca, Marco A Montes; Stutzle, Thomas; Van den Enden, Ken; Dorigo, Marco

    2011-04-01

    Incremental social learning (ISL) was proposed as a way to improve the scalability of systems composed of multiple learning agents. In this paper, we show that ISL can be very useful to improve the performance of population-based optimization algorithms. Our study focuses on two particle swarm optimization (PSO) algorithms: a) the incremental particle swarm optimizer (IPSO), which is a PSO algorithm with a growing population size in which the initial position of new particles is biased toward the best-so-far solution, and b) the incremental particle swarm optimizer with local search (IPSOLS), in which solutions are further improved through a local search procedure. We first derive analytically the probability density function induced by the proposed initialization rule applied to new particles. Then, we compare the performance of IPSO and IPSOLS on a set of benchmark functions with that of other PSO algorithms (with and without local search) and a random restart local search algorithm. Finally, we measure the benefits of using incremental social learning on PSO algorithms by running IPSO and IPSOLS on problems with different fitness distance correlations. PMID:20875976

  6. Lagrange Interpolation Learning Particle Swarm Optimization.

    PubMed

    Kai, Zhang; Jinchun, Song; Ke, Ni; Song, Li

    2016-01-01

    In recent years, comprehensive learning particle swarm optimization (CLPSO) has attracted the attention of many scholars for using in solving multimodal problems, as it is excellent in preserving the particles' diversity and thus preventing premature convergence. However, CLPSO exhibits low solution accuracy. Aiming to address this issue, we proposed a novel algorithm called LILPSO. First, this algorithm introduced a Lagrange interpolation method to perform a local search for the global best point (gbest). Second, to gain a better exemplar, one gbest, another two particle's historical best points (pbest) are chosen to perform Lagrange interpolation, then to gain a new exemplar, which replaces the CLPSO's comparison method. The numerical experiments conducted on various functions demonstrate the superiority of this algorithm, and the two methods are proven to be efficient for accelerating the convergence without leading the particle to premature convergence. PMID:27123982

  7. Unit Commitment by Adaptive Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Saber, Ahmed Yousuf; Senjyu, Tomonobu; Miyagi, Tsukasa; Urasaki, Naomitsu; Funabashi, Toshihisa

    This paper presents an Adaptive Particle Swarm Optimization (APSO) for Unit Commitment (UC) problem. APSO reliably and accurately tracks a continuously changing solution. By analyzing the social model of standard PSO for the UC problem of variable size and load demand, adaptive criteria are applied on PSO parameters and the global best particle (knowledge) based on the diversity of fitness. In this proposed method, PSO parameters are automatically adjusted using Gaussian modification. To increase the knowledge, the global best particle is updated instead of a fixed one in each generation. To avoid the method to be frozen, idle particles are reset. The real velocity is digitized (0/1) by a logistic function for binary UC. Finally, the benchmark data and methods are used to show the effectiveness of the proposed method.

  8. Particle Swarm Based Collective Searching Model for Adaptive Environment

    SciTech Connect

    Cui, Xiaohui; Patton, Robert M; Potok, Thomas E; Treadwell, Jim N

    2007-01-01

    This report presents a pilot study of an integration of particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the collective search behavior of self-organized groups in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social group adaptation for the dynamic environment and to provide insight and understanding of social group knowledge discovering and strategic searching. A new adaptive environment model, which dynamically reacts to the group collective searching behaviors, is proposed in this research. The simulations in the research indicate that effective communication between groups is not the necessary requirement for whole self-organized groups to achieve the efficient collective searching behavior in the adaptive environment.

  9. Particle Swarm Based Collective Searching Model for Adaptive Environment

    SciTech Connect

    Cui, Xiaohui; Patton, Robert M; Potok, Thomas E; Treadwell, Jim N

    2008-01-01

    This report presents a pilot study of an integration of particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the collective search behavior of self-organized groups in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social group adaptation for the dynamic environment and to provide insight and understanding of social group knowledge discovering and strategic searching. A new adaptive environment model, which dynamically reacts to the group collective searching behaviors, is proposed in this research. The simulations in the research indicate that effective communication between groups is not the necessary requirement for whole self-organized groups to achieve the efficient collective searching behavior in the adaptive environment.

  10. Multiswarm Particle Swarm Optimization with Transfer of the Best Particle

    PubMed Central

    Wei, Xiao-peng; Zhang, Jian-xia; Zhou, Dong-sheng; Zhang, Qiang

    2015-01-01

    We propose an improved algorithm, for a multiswarm particle swarm optimization with transfer of the best particle called BMPSO. In the proposed algorithm, we introduce parasitism into the standard particle swarm algorithm (PSO) in order to balance exploration and exploitation, as well as enhancing the capacity for global search to solve nonlinear optimization problems. First, the best particle guides other particles to prevent them from being trapped by local optima. We provide a detailed description of BMPSO. We also present a diversity analysis of the proposed BMPSO, which is explained based on the Sphere function. Finally, we tested the performance of the proposed algorithm with six standard test functions and an engineering problem. Compared with some other algorithms, the results showed that the proposed BMPSO performed better when applied to the test functions and the engineering problem. Furthermore, the proposed BMPSO can be applied to other nonlinear optimization problems. PMID:26345200

  11. Particle Swarm and Ant Colony Approaches in Multiobjective Optimization

    NASA Astrophysics Data System (ADS)

    Rao, S. S.

    2010-10-01

    The social behavior of groups of birds, ants, insects and fish has been used to develop evolutionary algorithms known as swarm intelligence techniques for solving optimization problems. This work presents the development of strategies for the application of two of the popular swarm intelligence techniques, namely the particle swarm and ant colony methods, for the solution of multiobjective optimization problems. In a multiobjective optimization problem, the objectives exhibit a conflicting nature and hence no design vector can minimize all the objectives simultaneously. The concept of Pareto-optimal solution is used in finding a compromise solution. A modified cooperative game theory approach, in which each objective is associated with a different player, is used in this work. The applicability and computational efficiencies of the proposed techniques are demonstrated through several illustrative examples involving unconstrained and constrained problems with single and multiple objectives and continuous and mixed design variables. The present methodologies are expected to be useful for the solution of a variety of practical continuous and mixed optimization problems involving single or multiple objectives with or without constraints.

  12. Roundness error assessment based on particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Zhao, J. W.; Chen, G. Q.

    2005-01-01

    Roundness error assessment is always a nonlinear optimization problem without constraints. The method of particle swarm optimization (PSO) is proposed to evaluate the roundness error. PSO is an evolution algorithm derived from the behavior of preying birds. PSO regards each feasible solution as a particle (point in n-dimensional space). It initializes a swarm of random particles in the feasible region. All particles always trace two particles in which one is the best position itself; another is the best position of all particles. According to the inertia weight and two best particles, all particles update their positions and velocities according to the fitness function. After iterations, it converges to an optimized solution. The reciprocal of the error assessment objective function is adopted as the fitness. In this paper the calculating procedures with PSO are given. Finally, an assessment example is used to verify this method. The results show that the method proposed provides a new way for other form and position error assessment because it can always converge to the global optimal solution.

  13. Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

    NASA Astrophysics Data System (ADS)

    Huang, Xiaobiao; Safranek, James

    2014-09-01

    Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

  14. Particle swarm optimization with recombination and dynamic linkage discovery.

    PubMed

    Chen, Ying-Ping; Peng, Wen-Chih; Jian, Ming-Chung

    2007-12-01

    In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system. PMID:18179066

  15. Bifurcating Particle Swarms in Smooth-Walled Fractures

    NASA Astrophysics Data System (ADS)

    Pyrak-Nolte, L. J.; Sun, H.

    2010-12-01

    Particle swarms can occur naturally or from industrial processes where small liquid drops containing thousands to millions of micron-size to colloidal-size particles are released over time from seepage or leaks into fractured rock. The behavior of these particle swarms as they fall under gravity are affected by particle interactions as well as interactions with the walls of the fractures. In this paper, we present experimental results on the effect of fractures on the cohesiveness of the swarm and the formation of bifurcation structures as they fall under gravity and interact with the fracture walls. A transparent cubic sample (100 mm x 100 mm x 100 mm) containing a synthetic fracture with uniform aperture distributions was optically imaged to quantify the effect of confinement within fractures on particle swarm formation, swarm velocity, and swarm geometry. A fracture with a uniform aperture distribution was fabricated from two polished rectangular prisms of acrylic. A series of experiments were performed to determine how swarm movement and geometry are affected as the walls of the fracture are brought closer together from 50 mm to 1 mm. During the experiments, the fracture was fully saturated with water. We created the swarms using two different particle sizes in dilute suspension (~ 1.0% by mass). The particles were 3 micron diameter fluorescent polymer beads and 25 micron diameter soda-lime glass beads. Experiments were performed using swarms that ranged in size from 5 µl to 60 µl. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera illuminated by a 100 mW diode-pumped doubled YAG laser. As a swarm falls in an open-tank of water, it forms a torroidal shape that is stable as long as no ambient or background currents exist in the water tank. When a swarm is released into a fracture with an aperture less than 5 mm, the swarm forms the torroidal shape but it is distorted because of the presence of the walls. The

  16. Lagrange Interpolation Learning Particle Swarm Optimization

    PubMed Central

    2016-01-01

    In recent years, comprehensive learning particle swarm optimization (CLPSO) has attracted the attention of many scholars for using in solving multimodal problems, as it is excellent in preserving the particles’ diversity and thus preventing premature convergence. However, CLPSO exhibits low solution accuracy. Aiming to address this issue, we proposed a novel algorithm called LILPSO. First, this algorithm introduced a Lagrange interpolation method to perform a local search for the global best point (gbest). Second, to gain a better exemplar, one gbest, another two particle’s historical best points (pbest) are chosen to perform Lagrange interpolation, then to gain a new exemplar, which replaces the CLPSO’s comparison method. The numerical experiments conducted on various functions demonstrate the superiority of this algorithm, and the two methods are proven to be efficient for accelerating the convergence without leading the particle to premature convergence. PMID:27123982

  17. Chaotic Particle Swarm Optimization with Mutation for Classification

    PubMed Central

    Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

    2015-01-01

    In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937

  18. Chaotic particle swarm optimization with mutation for classification.

    PubMed

    Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

    2015-01-01

    In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937

  19. Quantum-Behaved Particle Swarm Optimization with Chaotic Search

    NASA Astrophysics Data System (ADS)

    Yang, Kaiqiao; Nomura, Hirosato

    The chaotic search is introduced into Quantum-behaved Particle Swarm Optimization (QPSO) to increase the diversity of the swarm in the latter period of the search, so as to help the system escape from local optima. Taking full advantages of the characteristics of ergodicity and randomicity of chaotic variables, the chaotic search is carried out in the neighborhoods of the particles which are trapped into local optima. The experimental results on test functions show that QPSO with chaotic search outperforms the Particle Swarm Optimization (PSO) and QPSO.

  20. Transport of Particle Swarms Through Variable Aperture Fractures

    NASA Astrophysics Data System (ADS)

    Boomsma, E.; Pyrak-Nolte, L. J.

    2012-12-01

    Particle transport through fractured rock is a key concern with the increased use of micro- and nano-size particles in consumer products as well as from other activities in the sub- and near surface (e.g. mining, industrial waste, hydraulic fracturing, etc.). While particle transport is often studied as the transport of emulsions or dispersions, particles may also enter the subsurface from leaks or seepage that lead to particle swarms. Swarms are drop-like collections of millions of colloidal-sized particles that exhibit a number of unique characteristics when compared to dispersions and emulsions. Any contaminant or engineered particle that forms a swarm can be transported farther, faster, and more cohesively in fractures than would be expected from a traditional dispersion model. In this study, the effects of several variable aperture fractures on colloidal swarm cohesiveness and evolution were studied as a swarm fell under gravity and interacted with the fracture walls. Transparent acrylic was used to fabricate synthetic fracture samples with (1) a uniform aperture, (2) a converging region followed by a uniform region (funnel shaped), (3) a uniform region followed by a diverging region (inverted funnel), and (4) a cast of a an induced fracture from a carbonate rock. All of the samples consisted of two blocks that measured 100 x 100 x 50 mm. The minimum separation between these blocks determined the nominal aperture (0.5 mm to 20 mm). During experiments a fracture was fully submerged in water and swarms were released into it. The swarms consisted of a dilute suspension of 3 micron polystyrene fluorescent beads (1% by mass) with an initial volume of 5μL. The swarms were illuminated with a green (525 nm) LED array and imaged optically with a CCD camera. The variation in fracture aperture controlled swarm behavior. Diverging apertures caused a sudden loss of confinement that resulted in a rapid change in the swarm's shape as well as a sharp increase in its velocity

  1. Particle Swarm Transport through Immiscible Fluid Layers in a Fracture

    NASA Astrophysics Data System (ADS)

    Teasdale, N. D.; Boomsma, E.; Pyrak-Nolte, L. J.

    2011-12-01

    Immiscible fluids occur either naturally (e.g. oil & water) or from anthropogenic processes (e.g. liquid CO2 & water) in the subsurface and complicate the transport of natural or engineered micro- or nano-scale particles. In this study, we examined the effect of immiscible fluids on the formation and evolution of particle swarms in a fracture. A particle swarm is a collection of colloidal-size particles in a dilute suspension that exhibits cohesive behavior. Swarms fall under gravity with a velocity that is greater than the settling velocity of a single particle. Thus a particle swarm of colloidal contaminants can potentially travel farther and faster in a fracture than expected for a dispersion or emulsion of colloidal particles. We investigated the formation, evolution, and break-up of colloidal swarms under gravity in a uniform aperture fracture as hydrophobic/hydrophyllic particle swarms move across an oil-water interface. A uniform aperture fracture was fabricated from two transparent acrylic rectangular prisms (100 mm x 50 mm x 100 mm) that are separated by 1, 2.5, 5, 10 or 50 mm. The fracture was placed, vertically, inside a glass tank containing a layer of pure silicone oil (polydimethylsiloxane) on distilled water. Along the length of the fracture, 30 mm was filled with oil and 70 mm with water. Experiments were conducted using silicone oils with viscosities of 5, 10, 100, or 1000 cSt. Particle swarms (5 μl) were comprised of a 1% concentration (by mass) of 25 micron glass beads (hydrophilic) suspended in a water drop, or a 1% concentration (by mass) of 3 micron polystyrene fluorescent beads (hydrophobic) suspended in a water drop. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera and by green (525 nm) LED arrays for illumination. Swarms were spherical and remained coherent as they fell through the oil because of the immiscibility of oil and water. However, as a swarm approached the oil-water interface, it

  2. Particle Swarms in Fractures: Open Versus Partially Closed Systems

    NASA Astrophysics Data System (ADS)

    Boomsma, E.; Pyrak-Nolte, L. J.

    2014-12-01

    In the field, fractures may be isolated or connected to fluid reservoirs anywhere along the perimeter of a fracture. These boundaries affect fluid circulation, flow paths and communication with external reservoirs. The transport of drop like collections of colloidal-sized particles (particle swarms) in open and partially closed systems was studied. A uniform aperture synthetic fracture was constructed using two blocks (100 x 100 x 50 mm) of transparent acrylic placed parallel to each other. The fracture was fully submerged a tank filled with 100cSt silicone oil. Fracture apertures were varied from 5-80 mm. Partially closed systems were created by sealing the sides of the fracture with plastic film. The four boundary conditions study were: (Case 1) open, (Case 2) closed on the sides, (Case 3) closed on the bottom, and (Case 4) closed on both the sides and bottom of the fracture. A 15 μL dilute suspension of soda-lime glass particles in oil (2% by mass) were released into the fracture. Particle swarms were illuminated using a green (525 nm) LED array and imaged with a CCD camera. The presence of the additional boundaries modified the speed of the particle swarms (see figure). In Case 1, enhanced swarm transport was observed for a range of apertures, traveling faster than either very small or very large apertures. In Case 2, swarm velocities were enhanced over a larger range of fracture apertures than in any of the other cases. Case 3 shifted the enhanced transport regime to lower apertures and also reduced swarm speed when compared to Case 2. Finally, Case 4 eliminated the enhanced transport regime entirely. Communication between the fluid in the fracture and an external fluid reservoir resulted in enhanced swarm transport in Cases 1-3. The non-rigid nature of a swarm enables drag from the fracture walls to modify the swarm geometry. The particles composing a swarm reorganize in response to the fracture, elongating the swarm and maintaining its density. Unlike a

  3. Isolated particle swarm optimization with particle migration and global best adoption

    NASA Astrophysics Data System (ADS)

    Tsai, Hsing-Chih; Tyan, Yaw-Yauan; Wu, Yun-Wu; Lin, Yong-Huang

    2012-12-01

    Isolated particle swarm optimization (IPSO) segregates particles into several sub-swarms in order to improve the ability of the global optimization. In this study, particle migration and global best adoption (gbest adoption) are used to improve IPSO. Particle migration allows particles to travel among sub-swarms, based on the fitness of the sub-swarms. The use of gbest adoption allows sub-swarms to peep at the gbest proportionally or probably after a certain number of iterations, i.e. gbest replacing, and gbest sharing, respectively. Three well-known benchmark functions are utilized to determine the parameter settings of the IPSO. Then, 13 benchmark functions are used to study the performance of the designed IPSO. Computational experience demonstrates that the designed IPSO is superior to the original version of particle swarm optimization (PSO) in terms of the accuracy and stability of the results, when isolation phenomenon, particle migration and gbest sharing are involved.

  4. Composite Particle Swarm Optimizer With Historical Memory for Function Optimization.

    PubMed

    Li, Jie; Zhang, JunQi; Jiang, ChangJun; Zhou, MengChu

    2015-10-01

    Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms. PMID:26390177

  5. Improving Vector Evaluated Particle Swarm Optimisation Using Multiple Nondominated Leaders

    PubMed Central

    Lim, Kian Sheng; Buyamin, Salinda; Ahmad, Anita; Shapiai, Mohd Ibrahim; Naim, Faradila; Mubin, Marizan; Kim, Dong Hwa

    2014-01-01

    The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorporated to further improve the VEPSO algorithm. Hence, multiple nondominated solutions that are best at a respective objective function are used to guide particles in finding optimal solutions. The improved VEPSO is measured by the number of nondominated solutions found, generational distance, spread, and hypervolume. The results from the conducted experiments show that the proposed VEPSO significantly improved the existing VEPSO algorithms. PMID:24883386

  6. A Novel Particle Swarm Optimization Algorithm for Global Optimization

    PubMed Central

    Wang, Chun-Feng; Liu, Kui

    2016-01-01

    Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms. PMID:26955387

  7. A Novel Particle Swarm Optimization Algorithm for Global Optimization.

    PubMed

    Wang, Chun-Feng; Liu, Kui

    2016-01-01

    Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms. PMID:26955387

  8. Particle Swarm Social Adaptive Model for Multi-Agent Based Insurgency Warfare Simulation

    SciTech Connect

    Cui, Xiaohui; Potok, Thomas E

    2009-12-01

    To better understand insurgent activities and asymmetric warfare, a social adaptive model for modeling multiple insurgent groups attacking multiple military and civilian targets is proposed and investigated. This report presents a pilot study using the particle swarm modeling, a widely used non-linear optimal tool to model the emergence of insurgency campaign. The objective of this research is to apply the particle swarm metaphor as a model of insurgent social adaptation for the dynamically changing environment and to provide insight and understanding of insurgency warfare. Our results show that unified leadership, strategic planning, and effective communication between insurgent groups are not the necessary requirements for insurgents to efficiently attain their objective.

  9. Binary Particle Swarm Optimization based Biclustering of Web Usage Data

    NASA Astrophysics Data System (ADS)

    Rathipriya, R.; Thangavel, K.; Bagyamani, J.

    2011-07-01

    Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms.

  10. Markerless Human Motion Tracking Using Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization

    PubMed Central

    Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

    2015-01-01

    The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches—Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims. PMID:25978493

  11. Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

    PubMed

    Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah

    2015-01-01

    The high-dimensional search space involved in markerless full-body articulated human motion tracking from multiple-views video sequences has led to a number of solutions based on metaheuristics, the most recent form of which is Particle Swarm Optimization (PSO). However, the classical PSO suffers from premature convergence and it is trapped easily into local optima, significantly affecting the tracking accuracy. To overcome these drawbacks, we have developed a method for the problem based on Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO). The tracking problem is formulated as a non-linear 34-dimensional function optimization problem where the fitness function quantifies the difference between the observed image and a projection of the model configuration. Both the silhouette and edge likelihoods are used in the fitness function. Experiments using Brown and HumanEva-II dataset demonstrated that H-MCPSO performance is better than two leading alternative approaches-Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and self-recovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims. PMID:25978493

  12. Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem

    PubMed Central

    Ma, Rong-Jiang; Yu, Nan-Yang; Hu, Jun-Yi

    2013-01-01

    Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was improved. An actual case study was calculated to check its feasibility in practical use. The results show that the improved particle swarm optimization (IPSO) algorithm can more preferably solve the HSP problem than PSO algorithm. Moreover, the results also present the potential to provide useful information when making decisions in the practical planning process. Therefore, it is believed that if this approach is applied correctly and in combination with other elements, it can become a powerful and effective optimization tool for HSP problem. PMID:23935429

  13. Particle Swarm Social Model for Group Social Learning in Adaptive Environment

    SciTech Connect

    Cui, Xiaohui; Potok, Thomas E; Treadwell, Jim N; Patton, Robert M; Pullum, Laura L

    2008-01-01

    This report presents a study of integrating particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the social learning of self-organized groups and their collective searching behavior in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social learning for a dynamic environment. The research provides a platform for understanding and insights into knowledge discovery and strategic search in human self-organized social groups, such as insurgents or online communities.

  14. A multiobjective memetic algorithm based on particle swarm optimization.

    PubMed

    Liu, Dasheng; Tan, K C; Goh, C K; Ho, W K

    2007-02-01

    In this paper, a new memetic algorithm (MA) for multiobjective (MO) optimization is proposed, which combines the global search ability of particle swarm optimization with a synchronous local search heuristic for directed local fine-tuning. A new particle updating strategy is proposed based upon the concept of fuzzy global-best to deal with the problem of premature convergence and diversity maintenance within the swarm. The proposed features are examined to show their individual and combined effects in MO optimization. The comparative study shows the effectiveness of the proposed MA, which produces solution sets that are highly competitive in terms of convergence, diversity, and distribution. PMID:17278557

  15. Automatized Parameterization of DFTB Using Particle Swarm Optimization.

    PubMed

    Chou, Chien-Pin; Nishimura, Yoshifumi; Fan, Chin-Chai; Mazur, Grzegorz; Irle, Stephan; Witek, Henryk A

    2016-01-12

    We present a novel density-functional tight-binding (DFTB) parametrization toolkit developed to optimize the parameters of various DFTB models in a fully automatized fashion. The main features of the algorithm, based on the particle swarm optimization technique, are discussed, and a number of initial pilot applications of the developed methodology to molecular and solid systems are presented. PMID:26587758

  16. Reproducing fling-step and forward directivity at near source site using of multi-objective particle swarm optimization and multi taper

    NASA Astrophysics Data System (ADS)

    Nicknam, Ahmad; Hosseini, A.; Jamnani, H. Hamidi; Barkhordari, M. A.

    2013-12-01

    This paper presents a technique to reproduce compatible seismograms involving permanent displacement effects at sites close to the fault source. A multi-objective evolutionary algorithm is used to minimize the differences between the response spectra and multi-tapered power spectral densities corresponding to the recorded and simulated waveforms. The multi-taper method is used to reduce the spectral leakage that is inherent in the Fourier transformed form of waveforms, leading to a reduction of variance in power spectral amplitudes, thus permitting the calibration of the two sets of data. The technique is implemented using the 1998-Fandoqa (Iran) earthquake data and the results are compared with the actual observed data. Additionally, a comparison is made with a SAR interferometry study leading to fair agreement with the reported dislocation along the main fault. The simulation procedure and results are discussed and assessed concluding that, although the technique may be associated with uncertainties, it can still be used to reproduce waveforms at near source sites that include permanent dislocation, and can be used for seismic performance evaluation of structures in the region under study.

  17. A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm

    PubMed Central

    Ab Aziz, Nor Azlina; Mubin, Marizan; Mohamad, Mohd Saberi; Ab Aziz, Kamarulzaman

    2014-01-01

    In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm's best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SA-PSO, which is compared with the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well. PMID:25121109

  18. Joint global optimization of tomographic data based on particle swarm optimization and decision theory

    NASA Astrophysics Data System (ADS)

    Paasche, H.; Tronicke, J.

    2012-04-01

    In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto

  19. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

    PubMed Central

    Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

    2014-01-01

    Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

  20. Support vector machine based on adaptive acceleration particle swarm optimization.

    PubMed

    Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali

    2014-01-01

    Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

  1. Acoustic Radiation Optimization Using the Particle Swarm Optimization Algorithm

    NASA Astrophysics Data System (ADS)

    Jeon, Jin-Young; Okuma, Masaaki

    The present paper describes a fundamental study on structural bending design to reduce noise using a new evolutionary population-based heuristic algorithm called the particle swarm optimization algorithm (PSOA). The particle swarm optimization algorithm is a parallel evolutionary computation technique proposed by Kennedy and Eberhart in 1995. This algorithm is based on the social behavior models for bird flocking, fish schooling and other models investigated by zoologists. Optimal structural design problems to reduce noise are highly nonlinear, so that most conventional methods are difficult to apply. The present paper investigates the applicability of PSOA to such problems. Optimal bending design of a vibrating plate using PSOA is performed in order to minimize noise radiation. PSOA can be effectively applied to such nonlinear acoustic radiation optimization.

  2. Application of particle swarm techniques in sensor network configuration

    NASA Astrophysics Data System (ADS)

    Tillett, Jason; Yang, Shanchieh J.; Rao, Raghuveer; Sahin, Ferat

    2005-05-01

    A decentralized version of particle swarm optimization called the distributed particle swarm optimization (DPSO) approach is formulated and applied to the generation of sensor network configurations or topologies so that the deleterious effects of hidden nodes and asymmetric links on the performance of wireless sensor networks are minimized. Three different topology generation schemes, COMPOW, Cone-Based and the DPSO--based schemes are examined using ns-2. Simulations are executed by varying the node density and traffic rates. Results contrasting heterogeneous vs. homogeneous power reveal that an important metric for a sensor network topology may involve consideration of hidden nodes and asymmetric links, and demonstrate the effect of spatial reuse on the potency of topology generators.

  3. Particle Swarm Imaging (PSIM) - Innovative Gamma-Ray Assay - 13497

    SciTech Connect

    Parvin, Daniel; Clarke, Sean; Humes, Sarah J.

    2013-07-01

    Particle Swarm Imaging is an innovative technique used to perform quantitative gamma-ray assay. The innovation overcomes some of the difficulties associated with the accurate measurement and declaration of measurement uncertainties of radionuclide inventories within waste items when the distribution of activity is unknown. Implementation requires minimal equipment, with field measurements and results obtained using only a single electrically cooled HRGS gamma-ray detector. Examples of its application in the field are given in this paper. (authors)

  4. Particle swarm optimization for complex nonlinear optimization problems

    NASA Astrophysics Data System (ADS)

    Alexandridis, Alex; Famelis, Ioannis Th.; Tsitouras, Charalambos

    2016-06-01

    This work presents the application of a technique belonging to evolutionary computation, namely particle swarm optimization (PSO), to complex nonlinear optimization problems. To be more specific, a PSO optimizer is setup and applied to the derivation of Runge-Kutta pairs for the numerical solution of initial value problems. The effect of critical PSO operational parameters on the performance of the proposed scheme is thoroughly investigated.

  5. Genetic algorithm and particle swarm optimization combined with Powell method

    NASA Astrophysics Data System (ADS)

    Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui

    2013-10-01

    In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.

  6. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

    PubMed Central

    Garro, Beatriz A.; Vázquez, Roberto A.

    2015-01-01

    Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132

  7. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

    PubMed

    Garro, Beatriz A; Vázquez, Roberto A

    2015-01-01

    Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132

  8. A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations

    NASA Technical Reports Server (NTRS)

    Venter, Gerhard; Sobieszczanski-Sobieski, Jaroslaw

    2005-01-01

    A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tied to swarming theory. Although PSO algorithms present several attractive properties to the designer, they are plagued by high computational cost as measured by elapsed time. One approach to reduce the elapsed time is to make use of coarse-grained parallelization to evaluate the design points. Previous parallel PSO algorithms were mostly implemented in a synchronous manner, where all design points within a design iteration are evaluated before the next iteration is started. This approach leads to poor parallel speedup in cases where a heterogeneous parallel environment is used and/or where the analysis time depends on the design point being analyzed. This paper introduces an asynchronous parallel PSO algorithm that greatly improves the parallel e ciency. The asynchronous algorithm is benchmarked on a cluster assembled of Apple Macintosh G5 desktop computers, using the multi-disciplinary optimization of a typical transport aircraft wing as an example.

  9. A Novel Particle Swarm Optimization Approach for Grid Job Scheduling

    NASA Astrophysics Data System (ADS)

    Izakian, Hesam; Tork Ladani, Behrouz; Zamanifar, Kamran; Abraham, Ajith

    This paper represents a Particle Swarm Optimization (PSO) algorithm, for grid job scheduling. PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or near-optimal solutions. In this paper we used a PSO approach for grid job scheduling. The scheduler aims at minimizing makespan and flowtime simultaneously. Experimental studies show that the proposed novel approach is more efficient than the PSO approach reported in the literature.

  10. Particle swarm optimization of ascent trajectories of multistage launch vehicles

    NASA Astrophysics Data System (ADS)

    Pontani, Mauro

    2014-02-01

    Multistage launch vehicles are commonly employed to place spacecraft and satellites in their operational orbits. If the rocket characteristics are specified, the optimization of its ascending trajectory consists of determining the optimal control law that leads to maximizing the final mass at orbit injection. The numerical solution of a similar problem is not trivial and has been pursued with different methods, for decades. This paper is concerned with an original approach based on the joint use of swarming theory and the necessary conditions for optimality. The particle swarm optimization technique represents a heuristic population-based optimization method inspired by the natural motion of bird flocks. Each individual (or particle) that composes the swarm corresponds to a solution of the problem and is associated with a position and a velocity vector. The formula for velocity updating is the core of the method and is composed of three terms with stochastic weights. As a result, the population migrates toward different regions of the search space taking advantage of the mechanism of information sharing that affects the overall swarm dynamics. At the end of the process the best particle is selected and corresponds to the optimal solution to the problem of interest. In this work the three-dimensional trajectory of the multistage rocket is assumed to be composed of four arcs: (i) first stage propulsion, (ii) second stage propulsion, (iii) coast arc (after release of the second stage), and (iv) third stage propulsion. The Euler-Lagrange equations and the Pontryagin minimum principle, in conjunction with the Weierstrass-Erdmann corner conditions, are employed to express the thrust angles as functions of the adjoint variables conjugate to the dynamics equations. The use of these analytical conditions coming from the calculus of variations leads to obtaining the overall rocket dynamics as a function of seven parameters only, namely the unknown values of the initial state

  11. Particle swarm optimization for the clustering of wireless sensors

    NASA Astrophysics Data System (ADS)

    Tillett, Jason C.; Rao, Raghuveer M.; Sahin, Ferat; Rao, T. M.

    2003-07-01

    Clustering is necessary for data aggregation, hierarchical routing, optimizing sleep patterns, election of extremal sensors, optimizing coverage and resource allocation, reuse of frequency bands and codes, and conserving energy. Optimal clustering is typically an NP-hard problem. Solutions to NP-hard problems involve searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of NP-hard problems. We explore one such approach, Particle Swarm Optimization (PSO), an evolutionary programming technique where a 'swarm' of test solutions, analogous to a natural swarm of bees, ants or termites, is allowed to interact and cooperate to find the best solution to the given problem. We use the PSO approach to cluster sensors in a sensor network. The energy efficiency of our clustering in a data-aggregation type sensor network deployment is tested using a modified LEACH-C code. The PSO technique with a recursive bisection algorithm is tested against random search and simulated annealing; the PSO technique is shown to be robust. We further investigate developing a distributed version of the PSO algorithm for clustering optimally a wireless sensor network.

  12. Optimal Pid Tuning for Power System Stabilizers Using Adaptive Particle Swarm Optimization Technique

    NASA Astrophysics Data System (ADS)

    Oonsivilai, Anant; Marungsri, Boonruang

    2008-10-01

    An application of the intelligent search technique to find optimal parameters of power system stabilizer (PSS) considering proportional-integral-derivative controller (PID) for a single-machine infinite-bus system is presented. Also, an efficient intelligent search technique, adaptive particle swarm optimization (APSO), is engaged to express usefulness of the intelligent search techniques in tuning of the PID—PSS parameters. Improve damping frequency of system is optimized by minimizing an objective function with adaptive particle swarm optimization. At the same operating point, the PID—PSS parameters are also tuned by the Ziegler-Nichols method. The performance of proposed controller compared to the conventional Ziegler-Nichols PID tuning controller. The results reveal superior effectiveness of the proposed APSO based PID controller.

  13. Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization

    NASA Technical Reports Server (NTRS)

    Birge, Brian

    2013-01-01

    The design priority for manned space exploration missions is almost always placed on human safety. Proposed manned surface exploration tasks (lunar, asteroid sample returns, Mars) have the possibility of astronauts traveling several kilometers away from a home base. Deviations from preplanned paths are expected while exploring. In a time-critical emergency situation, there is a need to develop an optimal home base return path. The return path may or may not be similar to the outbound path, and what defines optimal may change with, and even within, each mission. A novel path planning algorithm and prototype program was developed using biologically inspired particle swarm optimization (PSO) that generates an optimal path of traversal while avoiding obstacles. Applications include emergency path planning on lunar, Martian, and/or asteroid surfaces, generating multiple scenarios for outbound missions, Earth-based search and rescue, as well as human manual traversal and/or path integration into robotic control systems. The strategy allows for a changing environment, and can be re-tasked at will and run in real-time situations. Given a random extraterrestrial planetary or small body surface position, the goal was to find the fastest (or shortest) path to an arbitrary position such as a safe zone or geographic objective, subject to possibly varying constraints. The problem requires a workable solution 100% of the time, though it does not require the absolute theoretical optimum. Obstacles should be avoided, but if they cannot be, then the algorithm needs to be smart enough to recognize this and deal with it. With some modifications, it works with non-stationary error topologies as well.

  14. GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS

    SciTech Connect

    Rogers, Adam; Fiege, Jason D.

    2011-02-01

    Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point-spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least-squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automatically, which represents the trade-off between the image {chi}{sup 2} and regularization effects, and allows an estimate of the optimally regularized solution for each lens parameter set. In the final step of the optimization procedure, the lens model with the lowest {chi}{sup 2} is used while the global optimizer solves for the source intensity distribution directly. This allows us to accurately determine the number of degrees of freedom in the problem to facilitate comparison between lens models and enforce positivity on the source profile. In practice, we find that the GA conducts a more thorough search of the parameter space than the PSO.

  15. Thermal design of an electric motor using Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Jandaud, P.-O.; Harmand, S.; Fakes, M.

    2012-11-01

    In this paper, flow inside an electric machine called starter-alternator is studied parametrically with CFD in order to be used by a thermal lumped model coupled to an optimization algorithm using Particle Swarm Optimization (PSO). In a first case, the geometrical parameters are symmetric allowing us to model only one side of the machine. The optimized thermal results are not conclusive. In a second case, all the parameters are independent. In this case, the flow is strongly influenced by the dissymmetry. Optimization results are this time a clear improvement compared to the original machine.

  16. What is Particle Swarm optimization? Application to hydrogeophysics (Invited)

    NASA Astrophysics Data System (ADS)

    Fernández Martïnez, J.; García Gonzalo, E.; Mukerji, T.

    2009-12-01

    Inverse problems are generally ill-posed. This yields lack of uniqueness and/or numerical instabilities. These features cause local optimization methods without prior information to provide unpredictable results, not being able to discriminate among the multiple models consistent with the end criteria. Stochastic approaches to inverse problems consist in shifting attention to the probability of existence of certain interesting subsurface structures instead of "looking for a unique model". Some well-known stochastic methods include genetic algorithms and simulated annealing. A more recent method, Particle Swarm Optimization, is a global optimization technique that has been successfully applied to solve inverse problems in many engineering fields, although its use in geosciences is still limited. Like all stochastic methods, PSO requires reasonably fast forward modeling. The basic idea behind PSO is that each model searches the model space according to its misfit history and the misfit of the other models of the swarm. PSO algorithm can be physically interpreted as a damped spring-mass system. This physical analogy was used to define a whole family of PSO optimizers and to establish criteria, based on the stability of particle swarm trajectories, to tune the PSO parameters: inertia, local and global accelerations. In this contribution we show application to different low-cost hydrogeophysical inverse problems: 1) a salt water intrusion problem using Vertical Electrical Soundings, 2) the inversion of Spontaneous Potential data for groundwater modeling, 3) the identification of Cole-Cole parameters for Induced Polarization data. We show that with this stochastic approach we are able to answer questions related to risk analysis, such as what is the depth of the salt intrusion with a certain probability, or giving probabilistic bounds for the water table depth. Moreover, these measures of uncertainty are obtained with small computational cost and time, allowing us a very

  17. Parameter Identification of Chaotic Systems by a Novel Dual Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Jiang, Yunxiang; Lau, Francis C. M.; Wang, Shiyuan; Tse, Chi K.

    In this paper, we propose a dual particle swarm optimization (PSO) algorithm for parameter identification of chaotic systems. We also consider altering the search range of individual particles adaptively according to their objective function value. We consider both noiseless and noisy channels between the original system and the estimation system. Finally, we verify the effectiveness of the proposed dual PSO method by estimating the parameters of the Lorenz system using two different data acquisition schemes. Simulation results show that the proposed method always outperforms the traditional PSO algorithm.

  18. System Identification of a DC Motor Using Different Variants of Particle Swarm Optimization Technique

    NASA Astrophysics Data System (ADS)

    Kar, Subhajit; Sharma, Kaushik Das

    2010-10-01

    System identification is a ubiquitous necessity for successful applications in various fields. The area of system identification can be characterized by a small number of leading principles, e.g. to look for sustainable descriptions by proper decisions in the triangle of model complexity, information contents in the data, and effective validation. Particle Swarm Optimization (PSO) is a stochastic, population-based optimization algorithm and many variants of PSO have been developed since, including constrained, multi objective, and discrete or combinatorial versions and applications have been developed using PSO in many fields. The basic PSO algorithm implicitly utilizes a fully connected neighborhood topology. However, local neighborhood models have also been proposed for PSO long ago, where each particle has access to the information corresponding to its immediate neighbors, according to a certain swarm topology. In this local neighborhood model of PSO, particles have information only of their own and their nearest neighbors' bests, rather than that of the entire population of the swarm. In the present work basic PSO method and two of its local neighborhood variants are utilized for determining the optimal parameters of a dc motor. The result obtain from the simulation study demonstrate the usefulness of the proposed methodology.

  19. Multivariable optimization of liquid rocket engines using particle swarm algorithms

    NASA Astrophysics Data System (ADS)

    Jones, Daniel Ray

    Liquid rocket engines are highly reliable, controllable, and efficient compared to other conventional forms of rocket propulsion. As such, they have seen wide use in the space industry and have become the standard propulsion system for launch vehicles, orbit insertion, and orbital maneuvering. Though these systems are well understood, historical optimization techniques are often inadequate due to the highly non-linear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finite-area combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizer-to-fuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSO-controlled parameters, engine performance was calculated based on propellant chemistry, combustion chamber pressure, and ambient pressure, which are provided as inputs to the program. The performance code was validated by comparison with NASA's Chemical Equilibrium with Applications (CEA) and the commercially available Rocket Propulsion Analysis (RPA) tool. Similarly, the PSO algorithm was validated by comparison with brute-force optimization, which calculates all possible solutions and subsequently determines which is the optimum. Particle Swarm Optimization was shown to be an effective optimizer capable of quick and reliable convergence for complex functions of multiple non-linear variables.

  20. Swarm intelligence for multi-objective optimization of synthesis gas production

    NASA Astrophysics Data System (ADS)

    Ganesan, T.; Vasant, P.; Elamvazuthi, I.; Ku Shaari, Ku Zilati

    2012-11-01

    In the chemical industry, the production of methanol, ammonia, hydrogen and higher hydrocarbons require synthesis gas (or syn gas). The main three syn gas production methods are carbon dioxide reforming (CRM), steam reforming (SRM) and partial-oxidation of methane (POM). In this work, multi-objective (MO) optimization of the combined CRM and POM was carried out. The empirical model and the MO problem formulation for this combined process were obtained from previous works. The central objectives considered in this problem are methane conversion, carbon monoxide selectivity and the hydrogen to carbon monoxide ratio. The MO nature of the problem was tackled using the Normal Boundary Intersection (NBI) method. Two techniques (Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO)) were then applied in conjunction with the NBI method. The performance of the two algorithms and the quality of the solutions were gauged by using two performance metrics. Comparative studies and results analysis were then carried out on the optimization results.

  1. Parallel global optimization with the particle swarm algorithm

    PubMed Central

    Schutte, J. F.; Reinbolt, J. A.; Fregly, B. J.; Haftka, R. T.; George, A. D.

    2007-01-01

    SUMMARY Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima—large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available. PMID:17891226

  2. Microwave-based medical diagnosis using particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Modiri, Arezoo

    This dissertation proposes and investigates a novel architecture intended for microwave-based medical diagnosis (MBMD). Furthermore, this investigation proposes novel modifications of particle swarm optimization algorithm for achieving enhanced convergence performance. MBMD has been investigated through a variety of innovative techniques in the literature since the 1990's and has shown significant promise in early detection of some specific health threats. In comparison to the X-ray- and gamma-ray-based diagnostic tools, MBMD does not expose patients to ionizing radiation; and due to the maturity of microwave technology, it lends itself to miniaturization of the supporting systems. This modality has been shown to be effective in detecting breast malignancy, and hence, this study focuses on the same modality. A novel radiator device and detection technique is proposed and investigated in this dissertation. As expected, hardware design and implementation are of paramount importance in such a study, and a good deal of research, analysis, and evaluation has been done in this regard which will be reported in ensuing chapters of this dissertation. It is noteworthy that an important element of any detection system is the algorithm used for extracting signatures. Herein, the strong intrinsic potential of the swarm-intelligence-based algorithms in solving complicated electromagnetic problems is brought to bear. This task is accomplished through addressing both mathematical and electromagnetic problems. These problems are called benchmark problems throughout this dissertation, since they have known answers. After evaluating the performance of the algorithm for the chosen benchmark problems, the algorithm is applied to MBMD tumor detection problem. The chosen benchmark problems have already been tackled by solution techniques other than particle swarm optimization (PSO) algorithm, the results of which can be found in the literature. However, due to the relatively high level

  3. Particle Swarm Inspired Underwater Sensor Self-Deployment

    PubMed Central

    Du, Huazheng; Xia, Na; Zheng, Rong

    2014-01-01

    Underwater sensor networks (UWSNs) can be applied in sea resource reconnaissance, pollution monitoring and assistant navigation, etc., and have become a hot research field in wireless sensor networks. In open and complicated underwater environments, targets (events) tend to be highly dynamic and uncertain. It is important to deploy sensors to cover potential events in an optimal manner. In this paper, the underwater sensor deployment problem and its performance evaluation metrics are introduced. Furthermore, a particle swarm inspired sensor self-deployment algorithm is presented. By simulating the flying behavior of particles and introducing crowd control, the proposed algorithm can drive sensors to cover almost all the events, and make the distribution of sensors match that of events. Through extensive simulations, we demonstrate that it can solve the underwater sensor deployment problem effectively, with fast convergence rate, and amiable to distributed implementation. PMID:25195852

  4. Particle swarm inspired underwater sensor self-deployment.

    PubMed

    Du, Huazheng; Xia, Na; Zheng, Rong

    2014-01-01

    Underwater sensor networks (UWSNs) can be applied in sea resource reconnaissance, pollution monitoring and assistant navigation, etc., and have become a hot research field in wireless sensor networks. In open and complicated underwater environments, targets (events) tend to be highly dynamic and uncertain. It is important to deploy sensors to cover potential events in an optimal manner. In this paper, the underwater sensor deployment problem and its performance evaluation metrics are introduced. Furthermore, a particle swarm inspired sensor self-deployment algorithm is presented. By simulating the flying behavior of particles and introducing crowd control, the proposed algorithm can drive sensors to cover almost all the events, and make the distribution of sensors match that of events. Through extensive simulations, we demonstrate that it can solve the underwater sensor deployment problem effectively, with fast convergence rate, and amiable to distributed implementation. PMID:25195852

  5. Particle Swarm Optimization Approach in a Consignment Inventory System

    NASA Astrophysics Data System (ADS)

    Sharifyazdi, Mehdi; Jafari, Azizollah; Molamohamadi, Zohreh; Rezaeiahari, Mandana; Arshizadeh, Rahman

    2009-09-01

    Consignment Inventory (CI) is a kind of inventory which is in the possession of the customer, but is still owned by the supplier. This creates a condition of shared risk whereby the supplier risks the capital investment associated with the inventory while the customer risks dedicating retail space to the product. This paper considers both the vendor's and the retailers' costs in an integrated model. The vendor here is a warehouse which stores one type of product and supplies it at the same wholesale price to multiple retailers who then sell the product in independent markets at retail prices. Our main aim is to design a CI system which generates minimum costs for the two parties. Here a Particle Swarm Optimization (PSO) algorithm is developed to calculate the proper values. Finally a sensitivity analysis is performed to examine the effects of each parameter on decision variables. Also PSO performance is compared with genetic algorithm.

  6. Electronic enclosure design using distributed particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Scriven, Ian; Lu, Junwei; Lewis, Andrew

    2013-02-01

    This article proposes a method for designing electromagnetic compatibility shielding enclosures using a peer-to-peer based distributed optimization system based on a modified particle swarm optimization algorithm. This optimization system is used to obtain optimal solutions to a shielding enclosure design problem efficiently with respect to both electromagnetic shielding efficiency and thermal performance. During the optimization procedure it becomes evident that optimization algorithms and computational models must be properly matched in order to achieve efficient operation. The proposed system is designed to be tolerant of faults and resource heterogeneity, and as such would find use in environments where large-scale computing resources are not available, such as smaller engineering companies, where it would allow computer-aided design by optimization using existing resources with little to no financial outlay.

  7. Solving constrained optimization problems with hybrid particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Zahara, Erwie; Hu, Chia-Hsin

    2008-11-01

    Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder-Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions.

  8. Order-2 Stability Analysis of Particle Swarm Optimization.

    PubMed

    Liu, Qunfeng

    2015-01-01

    Several stability analyses and stable regions of particle swarm optimization (PSO) have been proposed before. The assumption of stagnation and different definitions of stability are adopted in these analyses. In this paper, the order-2 stability of PSO is analyzed based on a weak stagnation assumption. A new definition of stability is proposed and an order-2 stable region is obtained. Several existing stable analyses for canonical PSO are compared, especially their definitions of stability and the corresponding stable regions. It is shown that the classical stagnation assumption is too strict and not necessary. Moreover, among all these definitions of stability, it is shown that our definition requires the weakest conditions, and additional conditions bring no benefit. Finally, numerical experiments are reported to show that the obtained stable region is meaningful. A new parameter combination of PSO is also shown to be good, even better than some known best parameter combinations. PMID:24738856

  9. Generalized Particle Swarm Algorithm for HCR Gearing Geometry Optimization

    NASA Astrophysics Data System (ADS)

    Kuzmanović, Siniša; Vereš, Miroslav; Rackov, Milan

    2012-12-01

    Temperature scuffing evidenced by damage to teeth flanks of gears is one of the mostimportant problems needing to be solved in the process of gearing design and calculation. Accordingto current valid standards, such calculations can be resolved with a high level of reliability for all theusual gearing types. However, suitable calculations for HCR gears have not been adequatelyresearched to date. It has been identified that in HCR gears some different process of scuffingformation occurs during the gear`s operation. In this article, the authors describe a new method forfinding optimal solutions for * a1 h , * a 2 h and x1, using a Generalized Particle Swarm OptimizationAlgorithm.

  10. A Triangle Mesh Standardization Method Based on Particle Swarm Optimization

    PubMed Central

    Duan, Liming; Bai, Yang; Wang, Haoyu; Shao, Hui; Zhong, Siyang

    2016-01-01

    To enhance the triangle quality of a reconstructed triangle mesh, a novel triangle mesh standardization method based on particle swarm optimization (PSO) is proposed. First, each vertex of the mesh and its first order vertices are fitted to a cubic curve surface by using least square method. Additionally, based on the condition that the local fitted surface is the searching region of PSO and the best average quality of the local triangles is the goal, the vertex position of the mesh is regulated. Finally, the threshold of the normal angle between the original vertex and regulated vertex is used to determine whether the vertex needs to be adjusted to preserve the detailed features of the mesh. Compared with existing methods, experimental results show that the proposed method can effectively improve the triangle quality of the mesh while preserving the geometric features and details of the original mesh. PMID:27509129

  11. Thermal and athermal three-dimensional swarms of self-propelled particles.

    PubMed

    Nguyen, Nguyen H P; Jankowski, Eric; Glotzer, Sharon C

    2012-07-01

    Swarms of self-propelled particles exhibit complex behavior that can arise from simple models, with large changes in swarm behavior resulting from small changes in model parameters. We investigate the steady-state swarms formed by self-propelled Morse particles in three dimensions using molecular dynamics simulations optimized for graphics processing units. We find a variety of swarms of different overall shape assemble spontaneously and that for certain Morse potential parameters at most two competing structures are observed. We report a rich "phase diagram" of athermal swarm structures observed across a broad range of interaction parameters. Unlike the structures formed in equilibrium self-assembly, we find that the probability of forming a self-propelled swarm can be biased by the choice of initial conditions. We investigate how thermal noise influences swarm formation and demonstrate ways it can be exploited to reconfigure one swarm into another. Our findings validate and extend previous observations of self-propelled Morse swarms and highlight open questions for predictive theories of nonequilibrium self-assembly. PMID:23005397

  12. Retrieval of particle size distribution from aerosol optical thickness using an improved particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Mao, Jiandong; Li, Jinxuan

    2015-10-01

    Particle size distribution is essential for describing direct and indirect radiation of aerosols. Because the relationship between the aerosol size distribution and optical thickness (AOT) is an ill-posed Fredholm integral equation of the first type, the traditional techniques for determining such size distributions, such as the Phillips-Twomey regularization method, are often ambiguous. Here, we use an approach based on an improved particle swarm optimization algorithm (IPSO) to retrieve aerosol size distribution. Using AOT data measured by a CE318 sun photometer in Yinchuan, we compared the aerosol size distributions retrieved using a simple genetic algorithm, a basic particle swarm optimization algorithm and the IPSO. Aerosol size distributions for different weather conditions were analyzed, including sunny, dusty and hazy conditions. Our results show that the IPSO-based inversion method retrieved aerosol size distributions under all weather conditions, showing great potential for similar size distribution inversions.

  13. Pixelated source optimization for optical lithography via particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Wang, Lei; Li, Sikun; Wang, Xiangzhao; Yan, Guanyong; Yang, Chaoxing

    2016-01-01

    Source optimization is one of the key techniques for achieving higher resolution without increasing the complexity of mask design. An efficient source optimization approach is proposed on the basis of particle swarm optimization. The pixelated sources are encoded into particles, which are evaluated by using the pattern error as the fitness function. Afterward, the optimization is implemented by updating the velocities and positions of these particles. This approach is demonstrated using three mask patterns, including a periodic array of contact holes, a vertical line/space design, and a complicated pattern. The pattern errors are reduced by 69.6%, 51.5%, and 40.3%, respectively. Compared with the source optimization approach via genetic algorithm, the proposed approach leads to faster convergence while improving the image quality at the same time. Compared with the source optimization approach via gradient descent method, the proposed approach does not need the calculation of gradients, and it has a strong adaptation to various lithographic models, fitness functions, and resist models. The robustness of the proposed approach to initial sources is also verified.

  14. A Hybrid Particle Swarm with Differential Evolution Operator Approach (DEPSO) for Linear Array Synthesis

    NASA Astrophysics Data System (ADS)

    Sarkar, Soham; Das, Swagatam

    In recent years particle swarm optimization emerges as one of the most efficient global optimization tools. In this paper, a hybrid particle swarm with differential evolution operator, termed DEPSO, is applied for the synthesis of linear array geometry. Here, the minimum side lobe level and null control, both are obtained by optimizing the spacing between the array elements by this technique. Moreover, a statistical comparison is also provided to establish its performance against the results obtained by Genetic Algorithm (GA), classical Particle Swarm Optimization (PSO), Tabu Search Algorithm (TSA), Differential Evolution (DE) and Memetic Algorithm (MA).

  15. Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions

    NASA Astrophysics Data System (ADS)

    Basu, Mousumi

    2015-07-01

    Particle swarm optimization (PSO) performs well for small dimensional and less complicated problems but fails to locate global minima for complex multi-minima functions. This paper proposes an improved particle swarm optimization (IPSO) which introduces Gaussian random variables in velocity term. This improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the speed of convergence and the simplicity of the structure of particle swarm optimization. The algorithm is experimentally validated on 17 benchmark functions and the results demonstrate good performance of the IPSO in solving unimodal and multimodal problems. Its high performance is verified by comparing with two popular PSO variants.

  16. The infrared spectral transmittance of Aspergillus niger spore aggregated particle swarm

    NASA Astrophysics Data System (ADS)

    Zhao, Xinying; Hu, Yihua; Gu, Youlin; Li, Le

    2015-10-01

    Microorganism aggregated particle swarm, which is quite an important composition of complex media environment, can be developed as a new kind of infrared functional materials. Current researches mainly focus on the optical properties of single microorganism particle. As for the swarm, especially the microorganism aggregated particle swarm, a more accurate simulation model should be proposed to calculate its extinction effect. At the same time, certain parameters deserve to be discussed, which helps to better develop the microorganism aggregated particle swarm as a new kind of infrared functional materials. In this paper, take Aspergillus Niger spore as an example. On the one hand, a new calculation model is established. Firstly, the cluster-cluster aggregation (CCA) model is used to simulate the structure of Aspergillus Niger spore aggregated particle. Secondly, the single scattering extinction parameters for Aspergillus Niger spore aggregated particle are calculated by using the discrete dipole approximation (DDA) method. Thirdly, the transmittance of Aspergillus Niger spore aggregated particle swarm is simulated by using Monte Carlo method. On the other hand, based on the model proposed above, what influences can wavelength causes has been studied, including the spectral distribution of scattering intensity of Aspergillus Niger spore aggregated particle and the infrared spectral transmittance of the aggregated particle swarm within the range of 8~14μm incident infrared wavelengths. Numerical results indicate that the scattering intensity of Aspergillus Niger spore aggregated particle reduces with the increase of incident wavelengths at each scattering angle. Scattering energy mainly concentrates on the scattering angle between 0~40°, forward scattering has an obvious effect. In addition, the infrared transmittance of Aspergillus Niger spore aggregated particle swarm goes up with the increase of incident wavelengths. However, some turning points of the trend

  17. Particle swarm optimization algorithm based low cost magnetometer calibration

    NASA Astrophysics Data System (ADS)

    Ali, A. S.; Siddharth, S., Syed, Z., El-Sheimy, N.

    2011-12-01

    Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a microprocessor provide inertial digital data from which position and orientation is obtained by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the absolute user heading based on Earth's magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are corrupted by several errors including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO) based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometer. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. The estimated bias and scale factor errors from the proposed algorithm improve the heading accuracy and the results are also statistically significant. Also, it can help in the development of the Pedestrian Navigation Devices (PNDs) when combined with the INS and GPS/Wi-Fi especially in the indoor environments

  18. A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.

    PubMed

    Amoshahy, Mohammad Javad; Shamsi, Mousa; Sedaaghi, Mohammad Hossein

    2016-01-01

    Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO's parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate. PMID:27560945

  19. Augmented Lagrangian Particle Swarm Optimization in Mechanism Design

    NASA Astrophysics Data System (ADS)

    Sedlaczek, Kai; Eberhard, Peter

    The problem of optimizing nonlinear multibody systems is in general nonlinear and nonconvex. This is especially true for the dimensional synthesis process of rigid body mechanisms, where often only local solutions might be found with gradient-based optimization methods. An attractive alternative for solving such multimodal optimization problems is the Particle Swarm Optimization (PSO) algorithm. This stochastic solution technique allows a derivative-free search for a global solution without the need for any initial design. In this work, we present an extension to the basic PSO algorithm in order to solve the problem of dimensional synthesis with nonlinear equality and inequality constraints. It utilizes the Augmented Lagrange Multiplier Method in combination with an advanced non-stationary penalty function approach that does not rely on excessively large penalty factors for sufficiently accurate solutions. Although the PSO method is even able to solve nonsmooth and discrete problems, this augmented algorithm can additionally calculate accurate Lagrange multiplier estimates for differentiable formulations, which are helpful in the analysis process of the optimization results. We demonstrate this method and show its very promising applicability to the constrained dimensional synthesis process of rigid body mechanisms.

  20. Particle swarm optimization with scale-free interactions.

    PubMed

    Liu, Chen; Du, Wen-Bo; Wang, Wen-Xu

    2014-01-01

    The particle swarm optimization (PSO) algorithm, in which individuals collaborate with their interacted neighbors like bird flocking to search for the optima, has been successfully applied in a wide range of fields pertaining to searching and convergence. Here we employ the scale-free network to represent the inter-individual interactions in the population, named SF-PSO. In contrast to the traditional PSO with fully-connected topology or regular topology, the scale-free topology used in SF-PSO incorporates the diversity of individuals in searching and information dissemination ability, leading to a quite different optimization process. Systematic results with respect to several standard test functions demonstrate that SF-PSO gives rise to a better balance between the convergence speed and the optimum quality, accounting for its much better performance than that of the traditional PSO algorithms. We further explore the dynamical searching process microscopically, finding that the cooperation of hub nodes and non-hub nodes play a crucial role in optimizing the convergence process. Our work may have implications in computational intelligence and complex networks. PMID:24859007

  1. Particle Swarm Optimization with Scale-Free Interactions

    PubMed Central

    Liu, Chen; Du, Wen-Bo; Wang, Wen-Xu

    2014-01-01

    The particle swarm optimization (PSO) algorithm, in which individuals collaborate with their interacted neighbors like bird flocking to search for the optima, has been successfully applied in a wide range of fields pertaining to searching and convergence. Here we employ the scale-free network to represent the inter-individual interactions in the population, named SF-PSO. In contrast to the traditional PSO with fully-connected topology or regular topology, the scale-free topology used in SF-PSO incorporates the diversity of individuals in searching and information dissemination ability, leading to a quite different optimization process. Systematic results with respect to several standard test functions demonstrate that SF-PSO gives rise to a better balance between the convergence speed and the optimum quality, accounting for its much better performance than that of the traditional PSO algorithms. We further explore the dynamical searching process microscopically, finding that the cooperation of hub nodes and non-hub nodes play a crucial role in optimizing the convergence process. Our work may have implications in computational intelligence and complex networks. PMID:24859007

  2. A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm

    PubMed Central

    Shamsi, Mousa; Sedaaghi, Mohammad Hossein

    2016-01-01

    Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO’s parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate. PMID:27560945

  3. Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection.

    PubMed

    Sun, Jun; Fang, Wei; Wu, Xiaojun; Palade, Vasile; Xu, Wenbo

    2012-01-01

    Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the bare-bones PSO family. Although it has been shown to perform well in finding the optimal solutions for many optimization problems, there has so far been little analysis on how it works in detail. This paper presents a comprehensive analysis of the QPSO algorithm. In the theoretical analysis, we analyze the behavior of a single particle in QPSO in terms of probability measure. Since the particle's behavior is influenced by the contraction-expansion (CE) coefficient, which is the most important parameter of the algorithm, the goal of the theoretical analysis is to find out the upper bound of the CE coefficient, within which the value of the CE coefficient selected can guarantee the convergence or boundedness of the particle's position. In the experimental analysis, the theoretical results are first validated by stochastic simulations for the particle's behavior. Then, based on the derived upper bound of the CE coefficient, we perform empirical studies on a suite of well-known benchmark functions to show how to control and select the value of the CE coefficient, in order to obtain generally good algorithmic performance in real world applications. Finally, a further performance comparison between QPSO and other variants of PSO on the benchmarks is made to show the efficiency of the QPSO algorithm with the proposed parameter control and selection methods. PMID:21905841

  4. An Improved Quantum-Behaved Particle Swarm Optimization Algorithm with Elitist Breeding for Unconstrained Optimization

    PubMed Central

    Yang, Zhen-Lun; Wu, Angus; Min, Hua-Qing

    2015-01-01

    An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPSO, when criteria met, the personal best of each particle and the global best of the swarm are used to generate new diverse individuals through the transposon operators. The new generated individuals with better fitness are selected to be the new personal best particles and global best particle to guide the swarm for further solution exploration. A comprehensive simulation study is conducted on a set of twelve benchmark functions. Compared with five state-of-the-art quantum-behaved particle swarm optimization algorithms, the proposed EB-QPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate. PMID:26064085

  5. A dynamic programming-based particle swarm optimization algorithm for an inventory management problem under uncertainty

    NASA Astrophysics Data System (ADS)

    Xu, Jiuping; Zeng, Ziqiang; Han, Bernard; Lei, Xiao

    2013-07-01

    This article presents a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm for solving an inventory management problem for large-scale construction projects under a fuzzy random environment. By taking into account the purchasing behaviour and strategy under rules of international bidding, a multi-objective fuzzy random dynamic programming model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform fuzzy random parameters into fuzzy variables that are subsequently defuzzified by using an expected value operator with optimistic-pessimistic index. The iterative nature of the authors' model motivates them to develop a DP-based PSO algorithm. More specifically, their approach treats the state variables as hidden parameters. This in turn eliminates many redundant feasibility checks during initialization and particle updates at each iteration. Results and sensitivity analysis are presented to highlight the performance of the authors' optimization method, which is very effective as compared to the standard PSO algorithm.

  6. Delay induced instabilities in self-propelling swarming particles

    NASA Astrophysics Data System (ADS)

    Forgoston, Eric; Schwartz, Ira

    2008-03-01

    We consider a general model of self-propelling biological or artificial individuals interacting through a pairwise attractive force in a two-dimensional system in the presence of noise and communication time delay. Previous work has shown that a large enough noise intensity will cause a translating swarm of individuals to transition to a rotating swarm with a stationary center of mass. In this work, we use numerical simulations to show that with the addition of a time delay, the model possesses a transition that depends on the size of the coupling parameter. This transition is independent of the swarm state (traveling or rotating) and is characterized by the alignment of all of the individuals along with a swarm oscillation. By considering the mean field equations without noise, we show that the time delay induced transition is associated with a Hopf bifurcation. The analytical result yields good agreement with numerical computations of the value of the coupling parameter at the Hopf point.

  7. An Adaptive Image Enhancement Technique by Combining Cuckoo Search and Particle Swarm Optimization Algorithm

    PubMed Central

    Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

    2015-01-01

    Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928

  8. An adaptive image enhancement technique by combining cuckoo search and particle swarm optimization algorithm.

    PubMed

    Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei

    2015-01-01

    Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper. PMID:25784928

  9. Performance evaluation of different types of particle representation procedures of Particle Swarm Optimization in Job-shop Scheduling Problems

    NASA Astrophysics Data System (ADS)

    Izah Anuar, Nurul; Saptari, Adi

    2016-02-01

    This paper addresses the types of particle representation (encoding) procedures in a population-based stochastic optimization technique in solving scheduling problems known in the job-shop manufacturing environment. It intends to evaluate and compare the performance of different particle representation procedures in Particle Swarm Optimization (PSO) in the case of solving Job-shop Scheduling Problems (JSP). Particle representation procedures refer to the mapping between the particle position in PSO and the scheduling solution in JSP. It is an important step to be carried out so that each particle in PSO can represent a schedule in JSP. Three procedures such as Operation and Particle Position Sequence (OPPS), random keys representation and random-key encoding scheme are used in this study. These procedures have been tested on FT06 and FT10 benchmark problems available in the OR-Library, where the objective function is to minimize the makespan by the use of MATLAB software. Based on the experimental results, it is discovered that OPPS gives the best performance in solving both benchmark problems. The contribution of this paper is the fact that it demonstrates to the practitioners involved in complex scheduling problems that different particle representation procedures can have significant effects on the performance of PSO in solving JSP.

  10. Optimal satellite formation reconfiguration using co-evolutionary particle swarm optimization in deep space

    NASA Astrophysics Data System (ADS)

    Huang, Haibin; Zhuang, Yufei

    2015-08-01

    This paper proposes a method that plans energy-optimal trajectories for multi-satellite formation reconfiguration in deep space environment. A novel co-evolutionary particle swarm optimization algorithm is stated to solve the nonlinear programming problem, so that the computational complexity of calculating the gradient information could be avoided. One swarm represents one satellite, and through communication with other swarms during the evolution, collisions between satellites can be avoided. In addition, a dynamic depth first search algorithm is proposed to solve the redundant search problem of a co-evolutionary particle swarm optimization method, with which the computation time can be shorten a lot. In order to make the actual trajectories optimal and collision-free with disturbance, a re-planning strategy is deduced for formation reconfiguration maneuver.

  11. Transmission Expansion Planning - A Multiyear Dynamic Approach Using a Discrete Evolutionary Particle Swarm Optimization Algorithm

    NASA Astrophysics Data System (ADS)

    Rocha, M. C.; Saraiva, J. T.

    2012-10-01

    The basic objective of Transmission Expansion Planning (TEP) is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO) meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.

  12. Reliability Optimization of Radial Distribution Systems Employing Differential Evolution and Bare Bones Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Kela, K. B.; Arya, L. D.

    2014-09-01

    This paper describes a methodology for determination of optimum failure rate and repair time for each section of a radial distribution system. An objective function in terms of reliability indices and their target values is selected. These indices depend mainly on failure rate and repair time of a section present in a distribution network. A cost is associated with the modification of failure rate and repair time. Hence the objective function is optimized subject to failure rate and repair time of each section of the distribution network considering the total budget allocated to achieve the task. The problem has been solved using differential evolution and bare bones particle swarm optimization. The algorithm has been implemented on a sample radial distribution system.

  13. Evaluation of a Particle Swarm Algorithm For Biomechanical Optimization

    PubMed Central

    Schutte, Jaco F.; Koh, Byung; Reinbolt, Jeffrey A.; Haftka, Raphael T.; George, Alan D.; Fregly, Benjamin J.

    2006-01-01

    Optimization is frequently employed in biomechanics research to solve system identification problems, predict human movement, or estimate muscle or other internal forces that cannot be measured directly. Unfortunately, biomechanical optimization problems often possess multiple local minima, making it difficult to find the best solution. Furthermore, convergence in gradient-based algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recently-developed version of the particle swarm optimization (PSO) algorithm to address these problems. The algorithm’s global search capabilities were investigated using a suite of difficult analytical test problems, while its scale-independent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three off-the-shelf optimization algorithms—a global genetic algorithm (GA) and multistart gradient-based sequential quadratic programming (SQP) and quasi-Newton (BFGS) algorithms. For the analytical test problems, only the PSO algorithm was successful on the majority of the problems. When compared to previously published results for the same problems, PSO was more robust than a global simulated annealing algorithm but less robust than a different, more complex genetic algorithm. For the biomechanical test problem, only the PSO algorithm was insensitive to design variable scaling, with the GA algorithm being mildly sensitive and the SQP and BFGS algorithms being highly sensitive. The proposed PSO algorithm provides a new off-the-shelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units. PMID:16060353

  14. Nontoxic colloidal particles impede antibiotic resistance of swarming bacteria by disrupting collective motion and speed

    NASA Astrophysics Data System (ADS)

    Lu, Shengtao; Liu, Fang; Xing, Bengang; Yeow, Edwin K. L.

    2015-12-01

    A monolayer of swarming B. subtilis on semisolid agar is shown to display enhanced resistance against antibacterial drugs due to their collective behavior and motility. The dynamics of swarming motion, visualized in real time using time-lapse microscopy, prevents the bacteria from prolonged exposure to lethal drug concentrations. The elevated drug resistance is significantly reduced when the collective motion of bacteria is judiciously disrupted using nontoxic polystyrene colloidal particles immobilized on the agar surface. The colloidal particles block and hinder the motion of the cells, and force large swarming rafts to break up into smaller packs in order to maneuver across narrow spaces between densely packed particles. In this manner, cohesive rafts rapidly lose their collectivity, speed, and group dynamics, and the cells become vulnerable to the drugs. The antibiotic resistance capability of swarming B. subtilis is experimentally observed to be negatively correlated with the number density of colloidal particles on the engineered surface. This relationship is further tested using an improved self-propelled particle model that takes into account interparticle alignment and hard-core repulsion. This work has pertinent implications on the design of optimal methods to treat drug resistant bacteria commonly found in swarming colonies.

  15. Purely hydrodynamic origin for swarming of swimming particles

    NASA Astrophysics Data System (ADS)

    Oyama, Norihiro; Molina, John Jairo; Yamamoto, Ryoichi

    2016-04-01

    Three-dimensional simulations with fully resolved hydrodynamics are performed to study the collective motion of model swimmers in bulk and confinement. Calculating the dynamic structure factor, we clarified that the swarming in bulk systems can be understood as a pseudoacoustic mode. Under confinement between flat parallel walls, this pseudoacoustic mode leads to a traveling wavelike motion. This swarming behavior is due purely to the hydrodynamic interactions between the swimmers and depends strongly on the type and strength of swimming (i.e., pusher or puller).

  16. Particle swarm optimization for discrete-time inverse optimal control of a doubly fed induction generator.

    PubMed

    Ruiz-Cruz, Riemann; Sanchez, Edgar N; Ornelas-Tellez, Fernando; Loukianov, Alexander G; Harley, Ronald G

    2013-12-01

    In this paper, the authors propose a particle swarm optimization (PSO) for a discrete-time inverse optimal control scheme of a doubly fed induction generator (DFIG). For the inverse optimal scheme, a control Lyapunov function (CLF) is proposed to obtain an inverse optimal control law in order to achieve trajectory tracking. A posteriori, it is established that this control law minimizes a meaningful cost function. The CLFs depend on matrix selection in order to achieve the control objectives; this matrix is determined by two mechanisms: initially, fixed parameters are proposed for this matrix by a trial-and-error method and then by using the PSO algorithm. The inverse optimal control scheme is illustrated via simulations for the DFIG, including the comparison between both mechanisms. PMID:24273145

  17. Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

    PubMed Central

    Jin, Junchen

    2016-01-01

    The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998

  18. Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm.

    PubMed

    Wang, Jiaxi; Lin, Boliang; Jin, Junchen

    2016-01-01

    The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998

  19. Particle swarm optimization method for the control of a fleet of Unmanned Aerial Vehicles

    NASA Astrophysics Data System (ADS)

    Belkadi, A.; Ciarletta, L.; Theilliol, D.

    2015-11-01

    This paper concerns a control approach of a fleet of Unmanned Aerial Vehicles (UAV) based on virtual leader. Among others, optimization methods are used to develop the virtual leader control approach, particularly the particle swarm optimization method (PSO). The goal is to find optimal positions at each instant of each UAV to guarantee the best performance of a given task by minimizing a predefined objective function. The UAVs are able to organize themselves on a 2D plane in a predefined architecture, following a mission led by a virtual leader and simultaneously avoiding collisions between various vehicles of the group. The global proposed method is independent from the model or the control of a particular UAV. The method is tested in simulation on a group of UAVs whose model is treated as a double integrator. Test results for the different cases are presented.

  20. Particle swarm-based structural optimization of laminated composite hydrokinetic turbine blades

    NASA Astrophysics Data System (ADS)

    Li, H.; Chandrashekhara, K.

    2015-09-01

    Composite blade manufacturing for hydrokinetic turbine application is quite complex and requires extensive optimization studies in terms of material selection, number of layers, stacking sequence, ply thickness and orientation. To avoid a repetitive trial-and-error method process, hydrokinetic turbine blade structural optimization using particle swarm optimization was proposed to perform detailed composite lay-up optimization. Layer numbers, ply thickness and ply orientations were optimized using standard particle swarm optimization to minimize the weight of the composite blade while satisfying failure evaluation. To address the discrete combinatorial optimization problem of blade stacking sequence, a novel permutation discrete particle swarm optimization model was also developed to maximize the out-of-plane load-carrying capability of the composite blade. A composite blade design with significant material saving and satisfactory performance was presented. The proposed methodology offers an alternative and efficient design solution to composite structural optimization which involves complex loading and multiple discrete and combinatorial design parameters.

  1. Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.

    PubMed

    Zhang, Jianlei; Zhang, Chunyan; Chu, Tianguang; Perc, Matjaž

    2011-01-01

    We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction. PMID:21760906

  2. Parameter estimation for chaotic systems with a Drift Particle Swarm Optimization method

    NASA Astrophysics Data System (ADS)

    Sun, Jun; Zhao, Ji; Wu, Xiaojun; Fang, Wei; Cai, Yujie; Xu, Wenbo

    2010-06-01

    Inspired by the motion of electrons in metal conductors in an electric field, we propose a variant of Particle Swarm Optimization (PSO), called Drift Particle Swarm Optimization (DPSO) algorithm, and apply it in estimating the unknown parameters of chaotic dynamic systems. The principle and procedure of DPSO are presented, and the algorithm is used to identify Lorenz system and Chen system. The experiment results show that for the given parameter configurations, DPSO can identify the parameters of the systems accurately and effectively, and it may be a promising tool for chaotic system identification as well as other numerical optimization problems in physics.

  3. Active Power Rescheduling for Avoiding Voltage Collapse Using Modified Bare Bones Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Arya, Rajesh; Purey, Pradeep

    2016-06-01

    MW-generation rescheduling is being considered for voltage stability improvement under stressed operating condition. At times it can avoid voltage collapse. This paper describes an algorithm for determination of optimum MW-generation participation pattern for static voltage stability margin enhancement. The optimum search direction has been obtained by employing modified bare born particle swarm optimization technique. Optimum search direction is based on maximization of distance to point of collapse in generation space. Developed algorithm has been implemented on a standard 25 bus test system. Results obtained have been compared with those obtained using standard particle swarm optimization.

  4. Active Power Rescheduling for Avoiding Voltage Collapse Using Modified Bare Bones Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Arya, Rajesh; Purey, Pradeep

    2015-06-01

    MW-generation rescheduling is being considered for voltage stability improvement under stressed operating condition. At times it can avoid voltage collapse. This paper describes an algorithm for determination of optimum MW-generation participation pattern for static voltage stability margin enhancement. The optimum search direction has been obtained by employing modified bare born particle swarm optimization technique. Optimum search direction is based on maximization of distance to point of collapse in generation space. Developed algorithm has been implemented on a standard 25 bus test system. Results obtained have been compared with those obtained using standard particle swarm optimization.

  5. Stochastic Set-Based Particle Swarm Optimization Based on Local Exploration for Solving the Carpool Service Problem.

    PubMed

    Chou, Sheng-Kai; Jiau, Ming-Kai; Huang, Shih-Chia

    2016-08-01

    The growing ubiquity of vehicles has led to increased concerns about environmental issues. These concerns can be mitigated by implementing an effective carpool service. In an intelligent carpool system, an automated service process assists carpool participants in determining routes and matches. It is a discrete optimization problem that involves a system-wide condition as well as participants' expectations. In this paper, we solve the carpool service problem (CSP) to provide satisfactory ride matches. To this end, we developed a particle swarm carpool algorithm based on stochastic set-based particle swarm optimization (PSO). Our method introduces stochastic coding to augment traditional particles, and uses three terminologies to represent a particle: 1) particle position; 2) particle view; and 3) particle velocity. In this way, the set-based PSO (S-PSO) can be realized by local exploration. In the simulation and experiments, two kind of discrete PSOs-S-PSO and binary PSO (BPSO)-and a genetic algorithm (GA) are compared and examined using tested benchmarks that simulate a real-world metropolis. We observed that the S-PSO outperformed the BPSO and the GA thoroughly. Moreover, our method yielded the best result in a statistical test and successfully obtained numerical results for meeting the optimization objectives of the CSP. PMID:26890944

  6. TOPICAL REVIEW: Kinetic phenomena in charged particle transport in gases, swarm parameters and cross section data

    NASA Astrophysics Data System (ADS)

    Petrovic, Z. Lj; Suvakov, M.; Nikitovic, Z.; Dujko, S.; Sasic, O.; Jovanovic, J.; Malovic, G.; Stojanovic, V.

    2007-02-01

    In this review we discuss the current status of the physics of charged particle swarms, mainly electrons. The whole field is analysed mainly through its relationship to plasma modelling and illustrated by some recent examples developed mainly by our group. The measurements of the swarm coefficients and the availability of the data are briefly discussed. More time is devoted to the development of complete electron molecule cross section sets along with recent examples such as NO, CF4 and HBr. We extend the discussion to the availability of ion and fast neutral data and how swarm experiments may serve to provide new data. As a point where new insight into the kinetics of charge particle transport is provided, the role of kinetic phenomena is discussed and recent examples are listed. We focus here on giving two examples on how non-conservative processes make dramatic effects in transport, the negative absolute mobility and the negative differential conductivity for positrons in argon. Finally we discuss the applicability of swarm data in plasma modelling and the relationship to other fields where swarm experiments and analysis make significant contributions. Based on the general invited lecture presented by the first author at ESCAMPIG 2006 at Lecce in Italy.

  7. Coarse-grained variables for particle-based models: diffusion maps and animal swarming simulations

    NASA Astrophysics Data System (ADS)

    Liu, Ping; Safford, Hannah R.; Couzin, Iain D.; Kevrekidis, Ioannis G.

    2014-12-01

    As microscopic (e.g. atomistic, stochastic, agent-based, particle-based) simulations become increasingly prevalent in the modeling of complex systems, so does the need to systematically coarse-grain the information they provide. Before even starting to formulate relevant coarse-grained equations, we need to determine the right macroscopic observables—the right variables in terms of which emergent behavior will be described. This paper illustrates the use of data mining (and, in particular, diffusion maps, a nonlinear manifold learning technique) in coarse-graining the dynamics of a particle-based model of animal swarming. Our computational data-driven coarse-graining approach extracts two coarse (collective) variables from the detailed particle-based simulations, and helps formulate a low-dimensional stochastic differential equation in terms of these two collective variables; this allows the efficient quantification of the interplay of "informed" and "naive" individuals in the collective swarm dynamics. We also present a brief exploration of swarm breakup and use data-mining in an attempt to identify useful predictors for it. In our discussion of the scope and limitations of the approach we focus on the key step of selecting an informative metric, allowing us to usefully compare different particle swarm configurations.

  8. Convective diffusion and adsorption in a swarm of spheroidal particles

    SciTech Connect

    Coutelieris, F.A.; Burganos, V.N.; Payatakes, A.C.

    1995-05-01

    The problem of mass transfer from a Newtonian fluid to a swarm of spheroidal adsorbers under creeping flow conditions is considered using the spheroid-in-cell model to represent the swarm. The flow field within the fluid envelope for the Kuwabara type of boundary conditions is obtained from the analytical solution of Dassios et al. (1994). The complete convective diffusion equation is used to describe mass transport within the envelope so that moderate and strong diffusion terms can be taken into account. A new set of boundary conditions is used that respects mass flux and concentration continuity across the outer surface of the cell and maximizes the applicability of the spheroid-in-cell model in the convection-to-diffusion transition regime. The resulting elliptic problem in two dimensions is solved numerically. Results for the upstream and downstream concentration profiles reveal that tangential diffusion is very significant and should not be neglected for moderate and low Peclet number values. Also, the classical Levich-type of formulation, which is theoretically valid for very weak diffusional terms only, can in practice be modified to predict with fair accuracy the overall Sherwood number and the adsorption efficiency of prolate and oblate spheroids-in-cell even in moderate Peclet number cases.

  9. Task partitioning in a robot swarm: object retrieval as a sequence of subtasks with direct object transfer.

    PubMed

    Pini, Giovanni; Brutschy, Arne; Scheidler, Alexander; Dorigo, Marco; Birattari, Mauro

    2014-01-01

    We study task partitioning in the context of swarm robotics. Task partitioning is the decomposition of a task into subtasks that can be tackled by different workers. We focus on the case in which a task is partitioned into a sequence of subtasks that must be executed in a certain order. This implies that the subtasks must interface with each other, and that the output of a subtask is used as input for the subtask that follows. A distinction can be made between task partitioning with direct transfer and with indirect transfer. We focus our study on the first case: The output of a subtask is directly transferred from an individual working on that subtask to an individual working on the subtask that follows. As a test bed for our study, we use a swarm of robots performing foraging. The robots have to harvest objects from a source, situated in an unknown location, and transport them to a home location. When a robot finds the source, it memorizes its position and uses dead reckoning to return there. Dead reckoning is appealing in robotics, since it is a cheap localization method and it does not require any additional external infrastructure. However, dead reckoning leads to errors that grow in time if not corrected periodically. We compare a foraging strategy that does not make use of task partitioning with one that does. We show that cooperation through task partitioning can be used to limit the effect of dead reckoning errors. This results in improved capability of locating the object source and in increased performance of the swarm. We use the implemented system as a test bed to study benefits and costs of task partitioning with direct transfer. We implement the system with real robots, demonstrating the feasibility of our approach in a foraging scenario. PMID:24730767

  10. Use of the particle swarm optimization algorithm for second order design of levelling networks

    NASA Astrophysics Data System (ADS)

    Yetkin, Mevlut; Inal, Cevat; Yigit, Cemal Ozer

    2009-08-01

    The weight problem in geodetic networks can be dealt with as an optimization procedure. This classic problem of geodetic network optimization is also known as second-order design. The basic principles of geodetic network optimization are reviewed. Then the particle swarm optimization (PSO) algorithm is applied to a geodetic levelling network in order to solve the second-order design problem. PSO, which is an iterative-stochastic search algorithm in swarm intelligence, emulates the collective behaviour of bird flocking, fish schooling or bee swarming, to converge probabilistically to the global optimum. Furthermore, it is a powerful method because it is easy to implement and computationally efficient. Second-order design of a geodetic levelling network using PSO yields a practically realizable solution. It is also suitable for non-linear matrix functions that are very often encountered in geodetic network optimization. The fundamentals of the method and a numeric example are given.

  11. Pseudo-gradient based particle swarm optimization for nonconvex economic dispatch

    NASA Astrophysics Data System (ADS)

    Vo, Dieu N.; Schegner, Peter; Ongsakul, Weerakorn

    2012-11-01

    This paper proposes a pseudo-gradient based particle swarm optimization (PGPSO) method for solving nonconvex economic dispatch (ED) including valve point effects, multiple fuels and prohibited operating zones. The proposed PGPSO is based on the self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) with position of particles guided by a pseudo-gradient. The pseudo-gradient here is to determine an appropriate direction for the particles during their movement so that they can quickly move to an optimal solution. The proposed method has been tested on several systems and the obtained results are compared to those from many other methods available in the literature. Test results have indicated that the proposed method can obtain less expensive total costs than many others in a faster computing manner, especially for the large-scale problems. Therefore, the proposed PGPSO is favorable for online implementation in the practical ED problems.

  12. A Lyapunov-Based Extension to Particle Swarm Dynamics for Continuous Function Optimization

    PubMed Central

    Bhattacharya, Sayantani; Konar, Amit; Das, Swagatam; Han, Sang Yong

    2009-01-01

    The paper proposes three alternative extensions to the classical global-best particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the well-known Lyapunov's stability theorem, provides a mathematical basis of the particle dynamics with a guaranteed convergence at an optimum. The inclusion of local and global attractors to this dynamics leads to faster convergence speed and better accuracy than the classical one. The second extension augments the velocity adaptation equation by a negative randomly weighted positional term of individual particle, while the third extension considers the negative positional term in place of the inertial term. Computer simulations further reveal that the last two extensions outperform both the classical and the first extension in terms of convergence speed and accuracy. PMID:22303158

  13. Identification of robust adaptation gene regulatory network parameters using an improved particle swarm optimization algorithm.

    PubMed

    Huang, X N; Ren, H P

    2016-01-01

    Robust adaptation is a critical ability of gene regulatory network (GRN) to survive in a fluctuating environment, which represents the system responding to an input stimulus rapidly and then returning to its pre-stimulus steady state timely. In this paper, the GRN is modeled using the Michaelis-Menten rate equations, which are highly nonlinear differential equations containing 12 undetermined parameters. The robust adaption is quantitatively described by two conflicting indices. To identify the parameter sets in order to confer the GRNs with robust adaptation is a multi-variable, multi-objective, and multi-peak optimization problem, which is difficult to acquire satisfactory solutions especially high-quality solutions. A new best-neighbor particle swarm optimization algorithm is proposed to implement this task. The proposed algorithm employs a Latin hypercube sampling method to generate the initial population. The particle crossover operation and elitist preservation strategy are also used in the proposed algorithm. The simulation results revealed that the proposed algorithm could identify multiple solutions in one time running. Moreover, it demonstrated a superior performance as compared to the previous methods in the sense of detecting more high-quality solutions within an acceptable time. The proposed methodology, owing to its universality and simplicity, is useful for providing the guidance to design GRN with superior robust adaptation. PMID:27323043

  14. Modified Sigmoid Function Based Gray Scale Image Contrast Enhancement Using Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Verma, Harish Kumar; Pal, Sandeep

    2016-06-01

    The main objective of an image enhancement is to improve eminence by maximizing the information content in the test image. Conventional contrast enhancement techniques either often fails to produce reasonable results for a broad variety of low-contrast and high contrast images, or cannot be automatically applied to different images, because they are parameters dependent. Hence this paper introduces a novel hybrid image enhancement approach by taking both the local and global information of an image. In the present work, sigmoid function is being modified on the basis of contrast of the images. The gray image enhancement problem is treated as nonlinear optimization problem with several constraints and solved by particle swarm optimization. The entropy and edge information is included in the objective function as quality measure of an image. The effectiveness of modified sigmoid function based enhancement over conventional methods namely linear contrast stretching, histogram equalization, and adaptive histogram equalization are better revealed by the enhanced images and further validated by statistical analysis of these images.

  15. Parameters optimisation of a vehicle suspension system using a particle swarm optimisation algorithm

    NASA Astrophysics Data System (ADS)

    Centeno Drehmer, Luis Roberto; Paucar Casas, Walter Jesus; Martins Gomes, Herbert

    2015-04-01

    The purpose of this paper is to determine the lumped suspension parameters that minimise a multi-objective function in a vehicle model under different standard PSD road profiles. This optimisation tries to meet the rms vertical acceleration weighted limits for human sensitivity curves from ISO 2631 [ISO-2631: guide for evaluation of human exposure to whole-body vibration. Europe; 1997] at the driver's seat, the road holding capability and the suspension working space. The vehicle is modelled in the frequency domain using eight degrees of freedom under a random road profile. The particle swarm optimisation and sequential quadratic programming algorithms are used to obtain the suspension optimal parameters in different road profile and vehicle velocity conditions. A sensitivity analysis is performed using the obtained results and, in Class G road profile, the seat damping has the major influence on the minimisation of the multi-objective function. The influence of vehicle parameters in vibration attenuation is analysed and it is concluded that the front suspension stiffness should be less stiff than the rear ones when the driver's seat relative position is located forward the centre of gravity of the car body. Graphs and tables for the behaviour of suspension parameters related to road classes, used algorithms and velocities are presented to illustrate the results. In Class A road profile it was possible to find optimal parameters within the boundaries of the design variables that resulted in acceptable values for the comfort, road holding and suspension working space.

  16. Linear Array Geometry Synthesis with Minimum Side Lobe Level and Null Control Using Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search

    NASA Astrophysics Data System (ADS)

    Ghosh, Pradipta; Zafar, Hamim

    Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This paper describes the synthesis method of linear array geometry with minimum side lobe level and null control by the Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search (DMSPSO) which optimizes the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. The results of the DMSPSO algorithm have been shown to meet or beat the results obtained using other state-of-the-art metaheuristics like the Genetic Algorithm (GA),General Particle Swarm Optimization (PSO), Memetic Algorithms (MA), and Tabu Search (TS) in a statistically meaningful way. Three design examples are presented that illustrate the use of the DMSPSO algorithm, and the optimization goal in each example is easily achieved.

  17. Structural optimization of Pt-Pd alloy nanoparticles using an improved discrete particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Shao, Gui-Fang; Wang, Ting-Na; Liu, Tun-Dong; Chen, Jun-Ren; Zheng, Ji-Wen; Wen, Yu-Hua

    2015-01-01

    Pt-Pd alloy nanoparticles, as potential catalyst candidates for new-energy resources such as fuel cells and lithium ion batteries owing to their excellent reactivity and selectivity, have aroused growing attention in the past years. Since structure determines physical and chemical properties of nanoparticles, the development of a reliable method for searching the stable structures of Pt-Pd alloy nanoparticles has become of increasing importance to exploring the origination of their properties. In this article, we have employed the particle swarm optimization algorithm to investigate the stable structures of alloy nanoparticles with fixed shape and atomic proportion. An improved discrete particle swarm optimization algorithm has been proposed and the corresponding scheme has been presented. Subsequently, the swap operator and swap sequence have been applied to reduce the probability of premature convergence to the local optima. Furthermore, the parameters of the exchange probability and the 'particle' size have also been considered in this article. Finally, tetrahexahedral Pt-Pd alloy nanoparticles has been used to test the effectiveness of the proposed method. The calculated results verify that the improved particle swarm optimization algorithm has superior convergence and stability compared with the traditional one.

  18. Pareto optimal calibration of highly nonlinear reactive transport groundwater models using particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Siade, A. J.; Prommer, H.; Welter, D.

    2014-12-01

    Groundwater management and remediation requires the implementation of numerical models in order to evaluate the potential anthropogenic impacts on aquifer systems. In many situations, the numerical model must, not only be able to simulate groundwater flow and transport, but also geochemical and biological processes. Each process being simulated carries with it a set of parameters that must be identified, along with differing potential sources of model-structure error. Various data types are often collected in the field and then used to calibrate the numerical model; however, these data types can represent very different processes and can subsequently be sensitive to the model parameters in extremely complex ways. Therefore, developing an appropriate weighting strategy to address the contributions of each data type to the overall least-squares objective function is not straightforward. This is further compounded by the presence of potential sources of model-structure errors that manifest themselves differently for each observation data type. Finally, reactive transport models are highly nonlinear, which can lead to convergence failure for algorithms operating on the assumption of local linearity. In this study, we propose a variation of the popular, particle swarm optimization algorithm to address trade-offs associated with the calibration of one data type over another. This method removes the need to specify weights between observation groups and instead, produces a multi-dimensional Pareto front that illustrates the trade-offs between data types. We use the PEST++ run manager, along with the standard PEST input/output structure, to implement parallel programming across multiple desktop computers using TCP/IP communications. This allows for very large swarms of particles without the need of a supercomputing facility. The method was applied to a case study in which modeling was used to gain insight into the mobilization of arsenic at a deepwell injection site

  19. The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.

    PubMed

    Han, Gaining; Fu, Weiping; Wang, Wen

    2016-01-01

    In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability. PMID:26880881

  20. The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm

    PubMed Central

    Han, Gaining; Fu, Weiping; Wang, Wen

    2016-01-01

    In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability. PMID:26880881

  1. Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization

    NASA Technical Reports Server (NTRS)

    Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard

    2002-01-01

    The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.

  2. Inverse problem of flame surface properties of wood using a repulsive particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Yoon, Kyung-Beom; Park, Won-Hee

    2015-04-01

    The convective heat transfer coefficient and surface emissivity before and after flame occurrence on a wood specimen surface and the flame heat flux were estimated using the repulsive particle swarm optimization algorithm and cone heater test results. The cone heater specified in the ISO 5660 standards was used, and six cone heater heat fluxes were tested. Preservative-treated Douglas fir 21 mm in thickness was used as the wood specimen in the tests. This study confirmed that the surface temperature of the specimen, which was calculated using the convective heat transfer coefficient, surface emissivity and flame heat flux on the wood specimen by a repulsive particle swarm optimization algorithm, was consistent with the measured temperature. Considering the measurement errors in the surface temperature of the specimen, the applicability of the optimization method considered in this study was evaluated.

  3. Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

    PubMed Central

    Zhang, Yudong; Wu, Lenan

    2011-01-01

    This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s. PMID:22163872

  4. A Fast Induction Motor Speed Estimation based on Hybrid Particle Swarm Optimization (HPSO)

    NASA Astrophysics Data System (ADS)

    Aryza, Solly; Abdallah, Ahmed N.; Khalidin, Zulkeflee bin; Lubis, Zulkarnain; Jie, Ma

    Intelligent control and estimation of power electronic systems by fuzzy logic and neural network techniques with fast torque and flux show tremendous promise in future. This paper proposed the application of Hybrid Particle Swarm Optimization (HPSO) for losses and operating cost minimization control in the induction motor drives. The main advantages of the proposed technique are; its simple structure and its straightforward maximization of induction motor efficiency and its operating cost for a given load torque. As will be demonstrated, Hybrid Particle Swarm Optimization (HPSO) is so efficient in finding the optimum operating machine's flux level. The results demonstrate the good quality and robustness in the system dynamic response and reduction in the steady-state and transient motor ripple torque.

  5. Comparative Studies of Particle Swarm Optimization Techniques for Reactive Power Allocation Planning in Power Systems

    NASA Astrophysics Data System (ADS)

    Fukuyama, Yoshikazu

    This paper compares particle swarm optimization (PSO) techniques for a reactive power allocation planning problem in power systems. The problem can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The PSO based methods determines a reactive power allocation strategy with continuous and discrete state variables such as automatic voltage regulator (AVR) operating values of electric power generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. Namely, this paper investigates applicability of PSO techniques to one of the practical MINLPs in power systems. Four variations of PSO: PSO with inertia weight approach (IWA), PSO with constriction factor approach (CFA), hybrid particle swarm optimization (HPSO) with IWA, and HPSO with CFA are compared. The four methods are applied to the standard IEEE14 bus system and a practical 112 bus system.

  6. Robotic U-shaped assembly line balancing using particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Mukund Nilakantan, J.; Ponnambalam, S. G.

    2016-02-01

    Automation in an assembly line can be achieved using robots. In robotic U-shaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a U-shaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. U-shaped assembly line problems are derived from traditional assembly line problems and are relatively new. Tasks are assigned to the workstations when either all of their predecessors or all of their successors have already been assigned to workstations. The objective function considered in this article is to maximize the cycle time of the assembly line, which in turn helps to maximize the production rate of the assembly line. RUALB aims at the optimal assignment of tasks to the workstations and selection of the best fit robot to the workstations in a manner such that the cycle time is minimized. To solve this problem, a particle swarm optimization algorithm embedded with a heuristic allocation (consecutive) procedure is proposed. The consecutive heuristic is used to allocate the tasks to the workstation and to assign a best fit robot to that workstation. The proposed algorithm is evaluated using a wide variety of data sets. The results indicate that robotic U-shaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.

  7. Trajectory planning of free-floating space robot using Particle Swarm Optimization (PSO)

    NASA Astrophysics Data System (ADS)

    Wang, Mingming; Luo, Jianjun; Walter, Ulrich

    2015-07-01

    This paper investigates the application of Particle Swarm Optimization (PSO) strategy to trajectory planning of the kinematically redundant space robot in free-floating mode. Due to the path dependent dynamic singularities, the volume of available workspace of the space robot is limited and enormous joint velocities are required when such singularities are met. In order to overcome this effect, the direct kinematics equations in conjunction with PSO are employed for trajectory planning of free-floating space robot. The joint trajectories are parametrized with the Bézier curve to simplify the calculation. Constrained PSO scheme with adaptive inertia weight is implemented to find the optimal solution of joint trajectories while specific objectives and imposed constraints are satisfied. The proposed method is not sensitive to the singularity issue due to the application of forward kinematic equations. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) redundant manipulator mounted on a free-floating spacecraft and demonstrate the effectiveness of the proposed method.

  8. Optimal multi-floor plant layout based on the mathematical programming and particle swarm optimization

    PubMed Central

    LEE, Chang Jun

    2015-01-01

    In the fields of researches associated with plant layout optimization, the main goal is to minimize the costs of pipelines and pumping between connecting equipment under various constraints. However, what is the lacking of considerations in previous researches is to transform various heuristics or safety regulations into mathematical equations. For example, proper safety distances between equipments have to be complied for preventing dangerous accidents on a complex plant. Moreover, most researches have handled single-floor plant. However, many multi-floor plants have been constructed for the last decade. Therefore, the proper algorithm handling various regulations and multi-floor plant should be developed. In this study, the Mixed Integer Non-Linear Programming (MINLP) problem including safety distances, maintenance spaces, etc. is suggested based on mathematical equations. The objective function is a summation of pipeline and pumping costs. Also, various safety and maintenance issues are transformed into inequality or equality constraints. However, it is really hard to solve this problem due to complex nonlinear constraints. Thus, it is impossible to use conventional MINLP solvers using derivatives of equations. In this study, the Particle Swarm Optimization (PSO) technique is employed. The ethylene oxide plant is illustrated to verify the efficacy of this study. PMID:26027708

  9. Optimal multi-floor plant layout based on the mathematical programming and particle swarm optimization.

    PubMed

    Lee, Chang Jun

    2015-01-01

    In the fields of researches associated with plant layout optimization, the main goal is to minimize the costs of pipelines and pumping between connecting equipment under various constraints. However, what is the lacking of considerations in previous researches is to transform various heuristics or safety regulations into mathematical equations. For example, proper safety distances between equipments have to be complied for preventing dangerous accidents on a complex plant. Moreover, most researches have handled single-floor plant. However, many multi-floor plants have been constructed for the last decade. Therefore, the proper algorithm handling various regulations and multi-floor plant should be developed. In this study, the Mixed Integer Non-Linear Programming (MINLP) problem including safety distances, maintenance spaces, etc. is suggested based on mathematical equations. The objective function is a summation of pipeline and pumping costs. Also, various safety and maintenance issues are transformed into inequality or equality constraints. However, it is really hard to solve this problem due to complex nonlinear constraints. Thus, it is impossible to use conventional MINLP solvers using derivatives of equations. In this study, the Particle Swarm Optimization (PSO) technique is employed. The ethylene oxide plant is illustrated to verify the efficacy of this study. PMID:26027708

  10. [Particle swarm optimization fuzzy modeling and closed-loop anaesthesia control based on cerebral state index].

    PubMed

    Tang, Jingtian; Cao, Yang; Xiao, Jiaying; Guo, Qulian

    2014-06-01

    Due to individual differences of the depth of anaesthesia (DOA) controlled objects, the drawbacks of monitoring index, the traditional PID controller of anesthesia depth could not meet the demands of nonlinear control. However, the adjustments of the rules of DOA fuzzy control often rely on personal experience and, therefore, it could not achieve the satisfactory control effects. The present research established a fuzzy closed-loop control system which takes the cerebral state index (CSI) value as a feedback controlled variable, and it also adopts the particle swarm optimization (PSO) to optimize the fuzzy control rule and membership functions between the change of CSI and propofol infusion rate. The system sets the CSI targets at 40 and 30 through the system simulation, and it also adds some Gaussian noise to imitate clinical disturbance. Experimental results indicated that this system could reach the set CSI point accurately, rapidly and stably, with no obvious perturbation in the presence of noise. The fuzzy controller based on CSI which has been optimized by PSO has better stability and robustness in the DOA closed loop control system. PMID:25219229

  11. Swarming visual sensor network for real-time multiple object tracking

    NASA Astrophysics Data System (ADS)

    Baranov, Yuri P.; Yarishev, Sergey N.; Medvedev, Roman V.

    2016-04-01

    Position control of multiple objects is one of the most actual problems in various technology areas. For example, in construction area this problem is represented as multi-point deformation control of bearing constructions in order to prevent collapse, in mining - deformation control of lining constructions, in rescue operations - potential victims and sources of ignition location, in transport - traffic control and traffic violations detection, in robotics -traffic control for organized group of robots and many other problems in different areas. Usage of stationary devices for solving these problems is inappropriately due to complex and variable geometry of control areas. In these cases self-organized systems of moving visual sensors is the best solution. This paper presents a concept of scalable visual sensor network with swarm architecture for multiple object pose estimation and real-time tracking. In this article recent developments of distributed measuring systems were reviewed with consequent investigation of advantages and disadvantages of existing systems, whereupon theoretical principles of design of swarming visual sensor network (SVSN) were declared. To measure object coordinates in the world coordinate system using TV-camera intrinsic (focal length, pixel size, principal point position, distortion) and extrinsic (rotation matrix, translation vector) calibration parameters were needed to be determined. Robust camera calibration was a too resource-intensive task for using moving camera. In this situation position of the camera is usually estimated using a visual mark with known parameters. All measurements were performed in markcentered coordinate systems. In this article a general adaptive algorithm of coordinate conversion of devices with various intrinsic parameters was developed. Various network topologies were reviewed. Minimum error in objet tracking was realized by finding the shortest path between object of tracking and bearing sensor, which set

  12. Optimal design of plate-fin heat exchangers by particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Yousefi, M.; Darus, A. N.

    2011-12-01

    This study explores the application of Particle Swarm Optimization (PSO) for optimization of a cross-flow plate fin heat exchanger. Minimization total annual cost is the target of optimization. Seven design parameters, namely, heat exchanger length at hot and cold sides, fin height, fin frequency, fin thickness, fin-strip length and number of hot side layers are selected as optimization variables. A case study from the literature proves the effectiveness of the proposed algorithm in case of achieving more accurate results.

  13. Thermal Depth Profiling Reconstruction by Multilayer Thermal Quadrupole Modeling and Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Chen, Zhao-Jiang; Zhang, Shu-Yi

    2010-02-01

    A new hybrid inversion method for depth profiling reconstruction of thermal conductivities of inhomogeneous solids is proposed based on multilayer quadrupole formalism of thermal waves, particle swarm optimization and sequential quadratic programming. The reconstruction simulations for several thermal conductivity profiles are performed to evaluate the applicability of the method. The numerical simulations demonstrate that the precision and insensitivity to noise of the inversion method are very satisfactory.

  14. Swarming behavior of gradient-responsive Brownian particles in a porous medium.

    PubMed

    Grančič, Peter; Štěpánek, František

    2012-07-01

    Active targeting by Brownian particles in a fluid-filled porous environment is investigated by computer simulation. The random motion of the particles is enhanced by diffusiophoresis with respect to concentration gradients of chemical signals released by the particles in the proximity of a target. The mathematical model, based on a combination of the Brownian dynamics method and a diffusion problem is formulated in terms of key parameters that include the particle diffusiophoretic mobility and the signaling threshold (the distance from the target at which the particles release their chemical signals). The results demonstrate that even a relatively simple chemical signaling scheme can lead to a complex collective behavior of the particles and can be a very efficient way of guiding a swarm of Brownian particles towards a target, similarly to the way colonies of living cells communicate via secondary messengers. PMID:23005461

  15. Swarming behavior of gradient-responsive Brownian particles in a porous medium

    NASA Astrophysics Data System (ADS)

    Grančič, Peter; Štěpánek, František

    2012-07-01

    Active targeting by Brownian particles in a fluid-filled porous environment is investigated by computer simulation. The random motion of the particles is enhanced by diffusiophoresis with respect to concentration gradients of chemical signals released by the particles in the proximity of a target. The mathematical model, based on a combination of the Brownian dynamics method and a diffusion problem is formulated in terms of key parameters that include the particle diffusiophoretic mobility and the signaling threshold (the distance from the target at which the particles release their chemical signals). The results demonstrate that even a relatively simple chemical signaling scheme can lead to a complex collective behavior of the particles and can be a very efficient way of guiding a swarm of Brownian particles towards a target, similarly to the way colonies of living cells communicate via secondary messengers.

  16. An attribute weight assignment and particle swarm optimization algorithm for medical database classifications.

    PubMed

    Chang, Pei-Chann; Lin, Jyun-Jie; Liu, Chen-Hao

    2012-09-01

    In this research, a hybrid model is developed by integrating a case-based reasoning approach and a particle swarm optimization model for medical data classification. Two data sets from UCI Machine Learning Repository, i.e., Liver Disorders Data Set and Breast Cancer Wisconsin (Diagnosis), are employed for benchmark test. Initially a case-based reasoning method is applied to preprocess the data set thus a weight vector for each feature is derived. A particle swarm optimization model is then applied to construct a decision-making system for diseases identified. The PSO algorithm starts by partitioning the data set into a relatively large number of clusters to reduce the effects of initial conditions and then reducing the number of clusters into two. The average forecasting accuracy for breast cancer of CBRPSO model is 97.4% and for liver disorders is 76.8%. The proposed case-based particle swarm optimization model is able to produce more accurate and comprehensible results for medical experts in medical diagnosis. PMID:21194784

  17. Estimation of design sea ice thickness with maximum entropy distribution by particle swarm optimization method

    NASA Astrophysics Data System (ADS)

    Tao, Shanshan; Dong, Sheng; Wang, Zhifeng; Jiang, Wensheng

    2016-06-01

    The maximum entropy distribution, which consists of various recognized theoretical distributions, is a better curve to estimate the design thickness of sea ice. Method of moment and empirical curve fitting method are common-used parameter estimation methods for maximum entropy distribution. In this study, we propose to use the particle swarm optimization method as a new parameter estimation method for the maximum entropy distribution, which has the advantage to avoid deviation introduced by simplifications made in other methods. We conducted a case study to fit the hindcasted thickness of the sea ice in the Liaodong Bay of Bohai Sea using these three parameter-estimation methods for the maximum entropy distribution. All methods implemented in this study pass the K-S tests at 0.05 significant level. In terms of the average sum of deviation squares, the empirical curve fitting method provides the best fit for the original data, while the method of moment provides the worst. Among all three methods, the particle swarm optimization method predicts the largest thickness of the sea ice for a same return period. As a result, we recommend using the particle swarm optimization method for the maximum entropy distribution for offshore structures mainly influenced by the sea ice in winter, but using the empirical curve fitting method to reduce the cost in the design of temporary and economic buildings.

  18. Application of particle swarm optimization to interpret Rayleigh wave dispersion curves

    NASA Astrophysics Data System (ADS)

    Song, Xianhai; Tang, Li; Lv, Xiaochun; Fang, Hongping; Gu, Hanming

    2012-09-01

    Rayleigh waves have been used increasingly as an appealing tool to obtain near-surface shear (S)-wave velocity profiles. However, inversion of Rayleigh wave dispersion curves is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this study, we proposed and tested a new Rayleigh wave dispersion curve inversion scheme based on particle swarm optimization (PSO). PSO is a global optimization strategy that simulates the social behavior observed in a flock (swarm) of birds searching for food. A simple search strategy in PSO guides the algorithm toward the best solution through constant updating of the cognitive knowledge and social behavior of the particles in the swarm. To evaluate calculation efficiency and stability of PSO to inversion of surface wave data, we first inverted three noise-free and three noise-corrupted synthetic data sets. Then, we made a comparative analysis with genetic algorithms (GA) and a Monte Carlo (MC) sampler and reconstructed a histogram of model parameters sampled on a low-misfit region less than 15% relative error to further investigate the performance of the proposed inverse procedure. Finally, we inverted a real-world example from a waste disposal site in NE Italy to examine the applicability of PSO on Rayleigh wave dispersion curves. Results from both synthetic and field data demonstrate that particle swarm optimization can be used for quantitative interpretation of Rayleigh wave dispersion curves. PSO seems superior to GA and MC in terms of both reliability and computational efforts. The great advantages of PSO are fast in locating the low misfit region and easy to implement. Also there are only three parameters to tune (inertia weight or constriction factor, local and global acceleration constants). Theoretical results exist to explain how to tune these parameters.

  19. SwarmPS: rapid, semi-automated single particle selection software.

    PubMed

    Woolford, David; Ericksson, Geoffery; Rothnagel, Rosalba; Muller, David; Landsberg, Michael J; Pantelic, Radosav S; McDowall, Alasdair; Pailthorpe, Bernard; Young, Paul R; Hankamer, Ben; Banks, Jasmine

    2007-01-01

    Single particle analysis (SPA) coupled with high-resolution electron cryo-microscopy is emerging as a powerful technique for the structure determination of membrane protein complexes and soluble macromolecular assemblies. Current estimates suggest that approximately 10(4)-10(5) particle projections are required to attain a 3A resolution 3D reconstruction (symmetry dependent). Selecting this number of molecular projections differing in size, shape and symmetry is a rate-limiting step for the automation of 3D image reconstruction. Here, we present Swarm(PS), a feature rich GUI based software package to manage large scale, semi-automated particle picking projects. The software provides cross-correlation and edge-detection algorithms. Algorithm-specific parameters are transparently and automatically determined through user interaction with the image, rather than by trial and error. Other features include multiple image handling (approximately 10(2)), local and global particle selection options, interactive image freezing, automatic particle centering, and full manual override to correct false positives and negatives. Swarm(PS) is user friendly, flexible, extensible, fast, and capable of exporting boxed out projection images, or particle coordinates, compatible with downstream image processing suites. PMID:16774837

  20. Binary particle swarm optimization algorithm assisted to design of plasmonic nanospheres sensor

    NASA Astrophysics Data System (ADS)

    Kaboli, Milad; Akhlaghi, Majid; Shahmirzaee, Hossein

    2016-04-01

    In this study, a coherent perfect absorption (CPA)-type sensor based on plasmonic nanoparticles is proposed. It consists of a plasmonic nanospheres array on top of a quartz substrate. The refractive index changes above the sensor surface, which is due to the appearance of gas or the absorption of biomolecules, can be detected by measuring the resulting spectral shifts of the absorption coefficient. Since the CPA efficiency depends strongly on the number of plasmonic nanoparticles and the locations of nanoparticles, binary particle swarm optimization (BPSO) algorithm is used to design an optimized array of the plasmonic nanospheres. This optimized structure should be maximizing the absorption coefficient only in the one frequency. BPSO algorithm, a swarm of birds including a matrix with binary entries responsible for controlling nanospheres in the array, shows the presence with symbol of ('1') and the absence with ('0'). The sensor can be used for sensing both gas and low refractive index materials in an aqueous environment.

  1. Multi-Robot, Multi-Target Particle Swarm Optimization Search in Noisy Wireless Environments

    SciTech Connect

    Kurt Derr; Milos Manic

    2009-05-01

    Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. The problem considered in this paper is exploration of an unknown environment with the goal of finding a target(s) at an unknown location(s) using multiple small mobile robots. This work demonstrates the use of a distributed PSO algorithm with a novel adaptive RSS weighting factor to guide robots for locating target(s) in high risk environments. The approach was developed and analyzed on multiple robot single and multiple target search. The approach was further enhanced by the multi-robot-multi-target search in noisy environments. The experimental results demonstrated how the availability of radio frequency signal can significantly affect robot search time to reach a target.

  2. Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks.

    PubMed

    Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming

    2016-01-01

    Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption. PMID:27428971

  3. Defect profile estimation from magnetic flux leakage signal via efficient managing particle swarm optimization.

    PubMed

    Han, Wenhua; Xu, Jun; Wang, Ping; Tian, Guiyun

    2014-01-01

    In this paper, efficient managing particle swarm optimization (EMPSO) for high dimension problem is proposed to estimate defect profile from magnetic flux leakage (MFL) signal. In the proposed EMPSO, in order to strengthen exchange of information among particles, particle pair model was built. For more efficient searching when facing different landscapes of problems, velocity updating scheme including three velocity updating models was also proposed. In addition, for more chances to search optimum solution out, automatic particle selection for re-initialization was implemented. The optimization results of six benchmark functions show EMPSO performs well when optimizing 100-D problems. The defect simulation results demonstrate that the inversing technique based on EMPSO outperforms the one based on self-learning particle swarm optimizer (SLPSO), and the estimated profiles are still close to the desired profiles with the presence of low noise in MFL signal. The results estimated from real MFL signal by EMPSO-based inversing technique also indicate that the algorithm is capable of providing an accurate solution of the defect profile with real signal. Both the simulation results and experiment results show the computing time of the EMPSO-based inversing technique is reduced by 20%-30% than that of the SLPSO-based inversing technique. PMID:24926693

  4. Thickness of the particle swarm in cosmic ray air showers

    NASA Technical Reports Server (NTRS)

    Linsley, J.

    1985-01-01

    The average dispersion in arrival time of air shower particles detected with a scintillator at an impact parameter r is described with accuracy 5-10% by the empirical formula sigma = Sigma sub to (1+r/r sub t) sup b, where Sigma sub to = 2.6 ns, r sub t = 30m and b = (1.94 + or - .08) (0.39 + or - .06) sec Theta, for r 2 km, 10 to the 8th power E 10 to the 11th power GeV, and Theta 60 deg. (E is the primary energy and theta is the zenith angle). The amount of fluctuation in sigma sub t due to fluctuations in the level of origin and shower development is less than 20%. These results provide a basis for estimating the impact parameters of very larger showers with data from very small detector arrays (mini-arrays). The energy of such showers can then be estimated from the local particle density. The formula also provides a basis for estimating the angular resolution of air shower array-telescopes.

  5. Dynamic topology multi force particle swarm optimization algorithm and its application

    NASA Astrophysics Data System (ADS)

    Chen, Dongning; Zhang, Ruixing; Yao, Chengyu; Zhao, Zheyu

    2016-01-01

    Particle swarm optimization (PSO) algorithm is an effective bio-inspired algorithm but it has shortage of premature convergence. Researchers have made some improvements especially in force rules and population topologies. However, the current algorithms only consider a single kind of force rules and lack consideration of comprehensive improvement in both multi force rules and population topologies. In this paper, a dynamic topology multi force particle swarm optimization (DTMFPSO) algorithm is proposed in order to get better search performance. First of all, the principle of the presented multi force particle swarm optimization (MFPSO) algorithm is that different force rules are used in different search stages, which can balance the ability of global and local search. Secondly, a fitness-driven edge-changing (FE) topology based on the probability selection mechanism of roulette method is designed to cut and add edges between the particles, and the DTMFPSO algorithm is proposed by combining the FE topology with the MFPSO algorithm through concurrent evolution of both algorithm and structure in order to further improve the search accuracy. Thirdly, Benchmark functions are employed to evaluate the performance of the DTMFPSO algorithm, and test results show that the proposed algorithm is better than the well-known PSO algorithms, such as µPSO, MPSO, and EPSO algorithms. Finally, the proposed algorithm is applied to optimize the process parameters for ultrasonic vibration cutting on SiC wafer, and the surface quality of the SiC wafer is improved by 12.8% compared with the PSO algorithm in Ref. [25]. This research proposes a DTMFPSO algorithm with multi force rules and dynamic population topologies evolved simultaneously, and it has better search performance.

  6. Optimum selection of mechanism type for heavy manipulators based on particle swarm optimization method

    NASA Astrophysics Data System (ADS)

    Zhao, Yong; Chen, Genliang; Wang, Hao; Lin, Zhongqin

    2013-07-01

    The mechanism type plays a decisive role in the mechanical performance of robotic manipulators. Feasible mechanism types can be obtained by applying appropriate type synthesis theory, but there is still a lack of effective and efficient methods for the optimum selection among different types of mechanism candidates. This paper presents a new strategy for the purpose of optimum mechanism type selection based on the modified particle swarm optimization method. The concept of sub-swarm is introduced to represent the different mechanisms generated by the type synthesis, and a competitive mechanism is employed between the sub-swarms to reassign their population size according to the relative performances of the mechanism candidates to implement the optimization. Combining with a modular modeling approach for fast calculation of the performance index of the potential candidates, the proposed method is applied to determine the optimum mechanism type among the potential candidates for the desired manipulator. The effectiveness and efficiency of the proposed method is demonstrated through a case study on the optimum selection of mechanism type of a heavy manipulator where six feasible candidates are considered with force capability as the specific performance index. The optimization result shows that the fitness of the optimum mechanism type for the considered heavy manipulator can be up to 0.578 5. This research provides the instruction in optimum selection of mechanism types for robotic manipulators.

  7. Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks

    PubMed Central

    Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming

    2016-01-01

    Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle’s position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption. PMID:27428971

  8. Diesel Engine performance improvement in a 1-D engine model using Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Karra, Prashanth

    2015-12-01

    A particle swarm optimization (PSO) technique was implemented to improve the engine development and optimization process to simultaneously reduce emissions and improve the fuel efficiency. The optimization was performed on a 4-stroke 4-cylinder GT-Power based 1-D diesel engine model. To achieve the multi-objective optimization, a merit function was defined which included the parameters to be optimized: Nitrogen Oxides (NOx), Nonmethyl hydro carbons (NMHC), Carbon Monoxide (CO), Brake Specific Fuel Consumption (BSFC). EPA Tier 3 emissions standards for non-road diesel engines between 37 and 75 kW of output were chosen as targets for the optimization. The combustion parameters analyzed in this study include: Start of main Injection, Start of Pilot Injection, Pilot fuel quantity, Swirl, and Tumble. The PSO was found to be very effective in quickly arriving at a solution that met the target criteria as defined in the merit function. The optimization took around 40-50 runs to find the most favourable engine operating condition under the constraints specified in the optimization. In a favourable case with a high merit function values, the NOx+NMHC and CO values were reduced to as low as 2.9 and 0.014 g/kWh, respectively. The operating conditions at this point were: 10 ATDC Main SOI, -25 ATDC Pilot SOI, 0.25 mg of pilot fuel, 0.45 Swirl and 0.85 tumble. These results indicate that late main injections preceded by a close, small pilot injection are most favourable conditions at the operating condition tested.

  9. A cloud theory-based particle swarm optimization for multiple decision maker vehicle routing problems with fuzzy random time windows

    NASA Astrophysics Data System (ADS)

    Ma, Yanfang; Xu, Jiuping

    2015-06-01

    This article puts forward a cloud theory-based particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bi-level programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theory-based particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency.

  10. Particle swarm optimization on low dimensional pose manifolds for monocular human pose estimation

    NASA Astrophysics Data System (ADS)

    Brauer, Jürgen; Hübner, Wolfgang; Arens, Michael

    2013-10-01

    Automatic assessment of situations with modern security and surveillance systems requires sophisticated discrimination capabilities. Therefore, action recognition, e.g. in terms of person-person or person-object interactions, is an essential core component of any surveillance system. A subclass of recent action recognition approaches are based on space time volumes, which are generated from trajectories of multiple anatomical landmarks like hands or shoulders. A general prerequisite of these methods is the robust estimation of the body pose, i.e. a simplified body model consisting of several anatomical landmarks. In this paper we address the problem of estimating 3D poses from monocular person image sequences. The first stage of our algorithm is the localization of body parts in the 2D image. For this, a part based object detection method is used, which in previous work has been shown to provide a sufficient basis for person detection and landmark estimation in a single step. The output of this processing step is a probability distribution for each landmark and image indicating possible locations of this landmark in image coordinates. The second stage of our algorithm searches for 3D pose estimates that best t to the 15 landmark probability distributions. For resolving ambiguities introduced by uncertainty in the locations of the landmarks, we perform an optimization within a Particle Swarm Optimization (PSO) framework, where each pose hypothesis is represented by a particle. Since the search in the high-dimensional 3D pose search space needs further guidance to deal with the inherently restricted 2D input information, we propose a new compact representation of motion sequences provided by motion capture databases. Poses of a motion sequence are embedded in a low-dimensional manifold. We represent each motion sequence by a compact representation referred to as pose splines using a small number of supporting point poses. The PSO algorithm can be extended to perform

  11. Optimal control of switched linear systems based on Migrant Particle Swarm Optimization algorithm

    NASA Astrophysics Data System (ADS)

    Xie, Fuqiang; Wang, Yongji; Zheng, Zongzhun; Li, Chuanfeng

    2009-10-01

    The optimal control problem for switched linear systems with internally forced switching has more constraints than with externally forced switching. Heavy computations and slow convergence in solving this problem is a major obstacle. In this paper we describe a new approach for solving this problem, which is called Migrant Particle Swarm Optimization (Migrant PSO). Imitating the behavior of a flock of migrant birds, the Migrant PSO applies naturally to both continuous and discrete spaces, in which definitive optimization algorithm and stochastic search method are combined. The efficacy of the proposed algorithm is illustrated via a numerical example.

  12. Early Mission Design of Transfers to Halo Orbits via Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Abraham, Andrew J.; Spencer, David B.; Hart, Terry J.

    2016-03-01

    Particle Swarm Optimization (PSO) is used to prune the search space of a low-thrust trajectory transfer from a high-altitude, Earth orbit to a Lagrange point orbit in the Earth-Moon system. Unlike a gradient based approach, this evolutionary PSO algorithm is capable of avoiding undesirable local minima. The PSO method is extended to a "local" version and uses a two dimensional search space that is capable of reducing the computation run-time by an order of magnitude when compared with published work. A technique for choosing appropriate PSO parameters is demonstrated and an example of an optimized trajectory is discussed.

  13. A particle swarm model for estimating reliability and scheduling system maintenance

    NASA Astrophysics Data System (ADS)

    Puzis, Rami; Shirtz, Dov; Elovici, Yuval

    2016-05-01

    Modifying data and information system components may introduce new errors and deteriorate the reliability of the system. Reliability can be efficiently regained with reliability centred maintenance, which requires reliability estimation for maintenance scheduling. A variant of the particle swarm model is used to estimate reliability of systems implemented according to the model view controller paradigm. Simulations based on data collected from an online system of a large financial institute are used to compare three component-level maintenance policies. Results show that appropriately scheduled component-level maintenance greatly reduces the cost of upholding an acceptable level of reliability by reducing the need in system-wide maintenance.

  14. A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models.

    PubMed

    Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung

    2015-01-01

    Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237

  15. A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models

    PubMed Central

    Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung

    2015-01-01

    Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237

  16. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization.

    PubMed

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  17. Approach to analytically minimize the LCD moiré by image-based particle swarm optimization.

    PubMed

    Tsai, Yu-Lin; Tien, Chung-Hao

    2015-10-01

    In this paper, we proposed a methodology to optimize the parametric window of a liquid crystal display (LCD) system, whose visual performance was deteriorated by the pixel moiré arising in between multiple periodic structures. Conventional analysis and minimization of moiré patterns are limited by few parameters. With the proposed image-based particle swarm optimization (PSO), we enable a multivariable optimization at the same time. A series of experiments was conducted to validate the methodology. Due to its versatility, the proposed technique will certainly have a promising impact on the fast optimization in LCD design with more complex configuration. PMID:26479663

  18. Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization

    PubMed Central

    Qiu, Jiaheng; Chen, Ray-Bing; Wang, Weichung; Wong, Weng Kee

    2014-01-01

    Particle swarm optimization (PSO) is an increasingly popular metaheuristic algorithm for solving complex optimization problems. Its popularity is due to its repeated successes in finding an optimum or a near optimal solution for problems in many applied disciplines. The algorithm makes no assumption of the function to be optimized and for biomedical experiments like those presented here, PSO typically finds the optimal solutions in a few seconds of CPU time on a garden-variety laptop. We apply PSO to find various types of optimal designs for several problems in the biological sciences and compare PSO performance relative to the differential evolution algorithm, another popular metaheuristic algorithm in the engineering literature. PMID:25285268

  19. Evolutionary artificial neural networks by multi-dimensional particle swarm optimization.

    PubMed

    Kiranyaz, Serkan; Ince, Turker; Yildirim, Alper; Gabbouj, Moncef

    2009-12-01

    In this paper, we propose a novel technique for the automatic design of Artificial Neural Networks (ANNs) by evolving to the optimal network configuration(s) within an architecture space. It is entirely based on a multi-dimensional Particle Swarm Optimization (MD PSO) technique, which re-forms the native structure of swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multidimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. With the proper encoding of the network configurations and parameters into particles, MD PSO can then seek the positional optimum in the error space and the dimensional optimum in the architecture space. The optimum dimension converged at the end of a MD PSO process corresponds to a unique ANN configuration where the network parameters (connections, weights and biases) can then be resolved from the positional optimum reached on that dimension. In addition to this, the proposed technique generates a ranked list of network configurations, from the best to the worst. This is indeed a crucial piece of information, indicating what potential configurations can be alternatives to the best one, and which configurations should not be used at all for a particular problem. In this study, the architecture space is defined over feed-forward, fully-connected ANNs so as to use the conventional techniques such as back-propagation and some other evolutionary methods in this field. The proposed technique is applied over the most challenging synthetic problems to test its optimality on evolving networks and over the benchmark problems to test its generalization capability as well as to make comparative evaluations with the several competing techniques. The experimental

  20. Bluetooth Based Chaos Synchronization Using Particle Swarm Optimization and Its Applications to Image Encryption

    PubMed Central

    Yau, Her-Terng; Hung, Tzu-Hsiang; Hsieh, Chia-Chun

    2012-01-01

    This study used the complex dynamic characteristics of chaotic systems and Bluetooth to explore the topic of wireless chaotic communication secrecy and develop a communication security system. The PID controller for chaos synchronization control was applied, and the optimum parameters of this PID controller were obtained using a Particle Swarm Optimization (PSO) algorithm. Bluetooth was used to realize wireless transmissions, and a chaotic wireless communication security system was developed in the design concept of a chaotic communication security system. The experimental results show that this scheme can be used successfully in image encryption. PMID:22969355

  1. Early Mission Design of Transfers to Halo Orbits via Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Abraham, Andrew J.; Spencer, David B.; Hart, Terry J.

    2016-06-01

    Particle Swarm Optimization (PSO) is used to prune the search space of a low-thrust trajectory transfer from a high-altitude, Earth orbit to a Lagrange point orbit in the Earth-Moon system. Unlike a gradient based approach, this evolutionary PSO algorithm is capable of avoiding undesirable local minima. The PSO method is extended to a "local" version and uses a two dimensional search space that is capable of reducing the computation run-time by an order of magnitude when compared with published work. A technique for choosing appropriate PSO parameters is demonstrated and an example of an optimized trajectory is discussed.

  2. Accurate FDTD modelling for dispersive media using rational function and particle swarm optimisation

    NASA Astrophysics Data System (ADS)

    Chung, Haejun; Ha, Sang-Gyu; Choi, Jaehoon; Jung, Kyung-Young

    2015-07-01

    This article presents an accurate finite-difference time domain (FDTD) dispersive modelling suitable for complex dispersive media. A quadratic complex rational function (QCRF) is used to characterise their dispersive relations. To obtain accurate coefficients of QCRF, in this work, we use an analytical approach and a particle swarm optimisation (PSO) simultaneously. In specific, an analytical approach is used to obtain the QCRF matrix-solving equation and PSO is applied to adjust a weighting function of this equation. Numerical examples are used to illustrate the validity of the proposed FDTD dispersion model.

  3. Improved particle swarm optimization algorithm for android medical care IOT using modified parameters.

    PubMed

    Sung, Wen-Tsai; Chiang, Yen-Chun

    2012-12-01

    This study examines wireless sensor network with real-time remote identification using the Android study of things (HCIOT) platform in community healthcare. An improved particle swarm optimization (PSO) method is proposed to efficiently enhance physiological multi-sensors data fusion measurement precision in the Internet of Things (IOT) system. Improved PSO (IPSO) includes: inertia weight factor design, shrinkage factor adjustment to allow improved PSO algorithm data fusion performance. The Android platform is employed to build multi-physiological signal processing and timely medical care of things analysis. Wireless sensor network signal transmission and Internet links allow community or family members to have timely medical care network services. PMID:22492176

  4. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    PubMed Central

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  5. Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion

    SciTech Connect

    Luo, Xiongbiao E-mail: Ying.Wan@student.uts.edu.au; Wan, Ying E-mail: Ying.Wan@student.uts.edu.au; He, Xiangjian

    2015-04-15

    Purpose: Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization. Methods: The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor’s) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm. Results: The experimental results demonstrate that the authors’ proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors’ framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29. Conclusions: A robust electromagnetically guided endoscopy framework was

  6. Application of Fuzzy C-Means Clustering Algorithm Based on Particle Swarm Optimization in Computer Forensics

    NASA Astrophysics Data System (ADS)

    Wang, Deguang; Han, Baochang; Huang, Ming

    Computer forensics is the technology of applying computer technology to access, investigate and analysis the evidence of computer crime. It mainly include the process of determine and obtain digital evidence, analyze and take data, file and submit result. And the data analysis is the key link of computer forensics. As the complexity of real data and the characteristics of fuzzy, evidence analysis has been difficult to obtain the desired results. This paper applies fuzzy c-means clustering algorithm based on particle swarm optimization (FCMP) in computer forensics, and it can be more satisfactory results.

  7. Optimal high speed CMOS inverter design using craziness based Particle Swarm Optimization Algorithm

    NASA Astrophysics Data System (ADS)

    De, Bishnu P.; Kar, Rajib; Mandal, Durbadal; Ghoshal, Sakti P.

    2015-07-01

    The inverter is the most fundamental logic gate that performs a Boolean operation on a single input variable. In this paper, an optimal design of CMOS inverter using an improved version of particle swarm optimization technique called Craziness based Particle Swarm Optimization (CRPSO) is proposed. CRPSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: it has fast, nearglobal convergence, and it uses nearly robust control parameters. The performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problems. To overcome these problems the PSO algorithm has been modiffed to CRPSO in this paper and is used for CMOS inverter design. In birds' flocking or ffsh schooling, a bird or a ffsh often changes direction suddenly. In the proposed technique, the sudden change of velocity is modelled by a direction reversal factor associated with the previous velocity and a "craziness" velocity factor associated with another direction reversal factor. The second condition is introduced depending on a predeffned craziness probability to maintain the diversity of particles. The performance of CRPSO is compared with real code.gnetic algorithm (RGA), and conventional PSO reported in the recent literature. CRPSO based design results are also compared with the PSPICE based results. The simulation results show that the CRPSO is superior to the other algorithms for the examples considered and can be efficiently used for the CMOS inverter design.

  8. Obtaining Approximate Values of Exterior Orientation Elements of Multi-Intersection Images Using Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Li, X.; Li, S. W.

    2012-07-01

    In this paper, an efficient global optimization algorithm in the field of artificial intelligence, named Particle Swarm Optimization (PSO), is introduced into close range photogrammetric data processing. PSO can be applied to obtain the approximate values of exterior orientation elements under the condition that multi-intersection photography and a small portable plane control frame are used. PSO, put forward by an American social psychologist J. Kennedy and an electrical engineer R.C. Eberhart, is a stochastic global optimization method based on swarm intelligence, which was inspired by social behavior of bird flocking or fish schooling. The strategy of obtaining the approximate values of exterior orientation elements using PSO is as follows: in terms of image coordinate observed values and space coordinates of few control points, the equations of calculating the image coordinate residual errors can be given. The sum of absolute value of each image coordinate is minimized to be the objective function. The difference between image coordinate observed value and the image coordinate computed through collinear condition equation is defined as the image coordinate residual error. Firstly a gross area of exterior orientation elements is given, and then the adjustment of other parameters is made to get the particles fly in the gross area. After iterative computation for certain times, the satisfied approximate values of exterior orientation elements are obtained. By doing so, the procedures like positioning and measuring space control points in close range photogrammetry can be avoided. Obviously, this method can improve the surveying efficiency greatly and at the same time can decrease the surveying cost. And during such a process, only one small portable control frame with a couple of control points is employed, and there are no strict requirements for the space distribution of control points. In order to verify the effectiveness of this algorithm, two experiments are

  9. Size character optimization for measurement system with binocular vision and optical elements based on local particle swarm method

    NASA Astrophysics Data System (ADS)

    Xu, Guan; Lu, Xue; Li, Xiaotao; Su, Jian; Tian, Guangdong; Sun, Lina

    2015-02-01

    The size character, which represents the relationship of the size variables of a binocular vision model, is studied to determine the optimal structure of the measurement system. An optimal objective function is constructed to minimize the system area. The optimal solution with the constraint of the virtual baseline distance is obtained from the particle swarm optimization (PSO) algorithm. A case study shows that when the virtual baseline is 1300 mm, the optimal parameters are: the real baseline distance is 600 mm, the bottom distance between the two smaller mirrors is 120 mm, the distance from a smaller mirror to the camera is 600 mm, the distance from a larger mirror to the camera is 700 mm, the angle between the smaller mirror and baseline is 15°, the angle between the large mirror and baseline is 30°, larger mirror is 500 mm long, then the optimal system area is 0.838 m2.

  10. Optimisation of full-toroidal continuously variable transmission in conjunction with fixed ratio mechanism using particle swarm optimisation

    NASA Astrophysics Data System (ADS)

    Delkhosh, Mojtaba; Saadat Foumani, Mahmoud

    2013-05-01

    The aim of this research is the optimisation of full-toroidal continuously variable transmission (CVT) in conjunction with the fixed ratio (FR) mechanism, while the optimisation objective is to minimise fuel consumption (FC) of the vehicle in the new European driving cycle. After the dynamic analysis of the power train, a computer model is developed to simulate contact between CVT elements and consequently calculate its efficiency. Then an algorithm is presented to calculate FC of the vehicle in the driving cycle. Then, an optimisation using particle swarm optimisation on the CVT geometry and FR mechanism (which is embedded between CVT and final drive) is carried out to minimise FC. It is found that by utilisation of the optimised CVT; FC will be about 11% and 8% lower, compared with the application of a five-speed manual transmission and conventional CVT, respectively. Finally, effects of the roller tilt angle and oil temperature on the FC are investigated.

  11. An Integer-Coded Chaotic Particle Swarm Optimization for Traveling Salesman Problem

    NASA Astrophysics Data System (ADS)

    Yue, Chen; Yan-Duo, Zhang; Jing, Lu; Hui, Tian

    Traveling Salesman Problem (TSP) is one of NP-hard combinatorial optimization problems, which will experience “combination explosion” when the problem goes beyond a certain size. Therefore, it has been a hot topic to search an effective solving method. The general mathematical model of TSP is discussed, and its permutation and combination based model is presented. Based on these, Integer-coded Chaotic Particle Swarm Optimization for solving TSP is proposed. Where, particle is encoded with integer; chaotic sequence is used to guide global search; and particle varies its positions via “flying”. With a typical 20-citys TSP as instance, the simulation experiment of comparing ICPSO with GA is carried out. Experimental results demonstrate that ICPSO is simple but effective, and better than GA at performance.

  12. Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems

    NASA Astrophysics Data System (ADS)

    Guo, Weian; Li, Wuzhao; Zhang, Qun; Wang, Lei; Wu, Qidi; Ren, Hongliang

    2014-11-01

    In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeography-based optimization (BBO) to propose a hybrid algorithm termed biogeography-based particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.

  13. Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block.

    PubMed

    Kora, Padmavathi; Kalva, Sri Ramakrishna

    2015-01-01

    Abnormal cardiac beat identification is a key process in the detection of heart diseases. Our present study describes a procedure for the detection of left and right bundle branch block (LBBB and RBBB) Electrocardiogram (ECG) patterns. The electrical impulses that control the cardiac beat face difficulty in moving inside the heart. This problem is termed as bundle branch block (BBB). BBB makes it harder for the heart to pump blood effectively through the heart circulatory system. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining two heuristic optimization methods: Bacterial Forging Optimization (BFO) and Particle Swarm Optimization (PSO) for the feature selection of ECG signals. One of the major controlling forces of BFO algorithm is the chemotactic movement of a bacterium that models a test solution. The chemotaxis process of the BFO depends on random search directions which may lead to a delay in achieving the global optimum solution. The hybrid technique: Bacterial Forging-Particle Swarm Optimization (BFPSO) incorporates the concepts from BFO and PSO and it creates individuals in a new generation. This BFPSO method performs local search through the chemotactic movement of BFO and the global search over the entire search domain is accomplished by a PSO operator. The BFPSO feature values are given as the input for the Levenberg-Marquardt Neural Network classifier. PMID:26361582

  14. Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks

    PubMed Central

    Robinson, Y. Harold; Rajaram, M.

    2015-01-01

    Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique. PMID:26819966

  15. 1-D DC Resistivity Modeling and Interpretation in Anisotropic Media Using Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Pekşen, Ertan; Yas, Türker; Kıyak, Alper

    2014-09-01

    We examine the one-dimensional direct current method in anisotropic earth formation. We derive an analytic expression of a simple, two-layered anisotropic earth model. Further, we also consider a horizontally layered anisotropic earth response with respect to the digital filter method, which yields a quasi-analytic solution over anisotropic media. These analytic and quasi-analytic solutions are useful tests for numerical codes. A two-dimensional finite difference earth model in anisotropic media is presented in order to generate a synthetic data set for a simple one-dimensional earth. Further, we propose a particle swarm optimization method for estimating the model parameters of a layered anisotropic earth model such as horizontal and vertical resistivities, and thickness. The particle swarm optimization is a naturally inspired meta-heuristic algorithm. The proposed method finds model parameters quite successfully based on synthetic and field data. However, adding 5 % Gaussian noise to the synthetic data increases the ambiguity of the value of the model parameters. For this reason, the results should be controlled by a number of statistical tests. In this study, we use probability density function within 95 % confidence interval, parameter variation of each iteration and frequency distribution of the model parameters to reduce the ambiguity. The result is promising and the proposed method can be used for evaluating one-dimensional direct current data in anisotropic media.

  16. Modelling arterial pressure waveforms using Gaussian functions and two-stage particle swarm optimizer.

    PubMed

    Liu, Chengyu; Zhuang, Tao; Zhao, Lina; Chang, Faliang; Liu, Changchun; Wei, Shoushui; Li, Qiqiang; Zheng, Dingchang

    2014-01-01

    Changes of arterial pressure waveform characteristics have been accepted as risk indicators of cardiovascular diseases. Waveform modelling using Gaussian functions has been used to decompose arterial pressure pulses into different numbers of subwaves and hence quantify waveform characteristics. However, the fitting accuracy and computation efficiency of current modelling approaches need to be improved. This study aimed to develop a novel two-stage particle swarm optimizer (TSPSO) to determine optimal parameters of Gaussian functions. The evaluation was performed on carotid and radial artery pressure waveforms (CAPW and RAPW) which were simultaneously recorded from twenty normal volunteers. The fitting accuracy and calculation efficiency of our TSPSO were compared with three published optimization methods: the Nelder-Mead, the modified PSO (MPSO), and the dynamic multiswarm particle swarm optimizer (DMS-PSO). The results showed that TSPSO achieved the best fitting accuracy with a mean absolute error (MAE) of 1.1% for CAPW and 1.0% for RAPW, in comparison with 4.2% and 4.1% for Nelder-Mead, 2.0% and 1.9% for MPSO, and 1.2% and 1.1% for DMS-PSO. In addition, to achieve target MAE of 2.0%, the computation time of TSPSO was only 1.5 s, which was only 20% and 30% of that for MPSO and DMS-PSO, respectively. PMID:24967415

  17. Modelling Arterial Pressure Waveforms Using Gaussian Functions and Two-Stage Particle Swarm Optimizer

    PubMed Central

    Zhuang, Tao; Zhao, Lina; Chang, Faliang; Liu, Changchun; Wei, Shoushui; Li, Qiqiang

    2014-01-01

    Changes of arterial pressure waveform characteristics have been accepted as risk indicators of cardiovascular diseases. Waveform modelling using Gaussian functions has been used to decompose arterial pressure pulses into different numbers of subwaves and hence quantify waveform characteristics. However, the fitting accuracy and computation efficiency of current modelling approaches need to be improved. This study aimed to develop a novel two-stage particle swarm optimizer (TSPSO) to determine optimal parameters of Gaussian functions. The evaluation was performed on carotid and radial artery pressure waveforms (CAPW and RAPW) which were simultaneously recorded from twenty normal volunteers. The fitting accuracy and calculation efficiency of our TSPSO were compared with three published optimization methods: the Nelder-Mead, the modified PSO (MPSO), and the dynamic multiswarm particle swarm optimizer (DMS-PSO). The results showed that TSPSO achieved the best fitting accuracy with a mean absolute error (MAE) of 1.1% for CAPW and 1.0% for RAPW, in comparison with 4.2% and 4.1% for Nelder-Mead, 2.0% and 1.9% for MPSO, and 1.2% and 1.1% for DMS-PSO. In addition, to achieve target MAE of 2.0%, the computation time of TSPSO was only 1.5 s, which was only 20% and 30% of that for MPSO and DMS-PSO, respectively. PMID:24967415

  18. Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

    PubMed

    Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

  19. Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

    PubMed Central

    Adam, Asrul; Mohd Tumari, Mohd Zaidi; Mohamad, Mohd Saberi

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

  20. Parameter identification of robot manipulators: a heuristic particle swarm search approach.

    PubMed

    Yan, Danping; Lu, Yongzhong; Levy, David

    2015-01-01

    Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles' local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators. PMID:26039090

  1. Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks.

    PubMed

    Robinson, Y Harold; Rajaram, M

    2015-01-01

    Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique. PMID:26819966

  2. Biochemical systems identification by a random drift particle swarm optimization approach

    PubMed Central

    2014-01-01

    Background Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. Results This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. Conclusions The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study. PMID:25078435

  3. Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression.

    PubMed

    Cedeño, Walter; Agrafiotis, Dimitris K

    2003-01-01

    We describe the application of particle swarms for the development of quantitative structure-activity relationship (QSAR) models based on k-nearest neighbor and kernel regression. Particle swarms is a population-based stochastic search method based on the principles of social interaction. Each individual explores the feature space guided by its previous success and that of its neighbors. Success is measured using leave-one-out (LOO) cross validation on the resulting model as determined by k-nearest neighbor kernel regression. The technique is shown to compare favorably to simulated annealing using three classical data sets from the QSAR literature. PMID:13677491

  4. hydroPSO: A Versatile Particle Swarm Optimisation R Package for Calibration of Environmental Models

    NASA Astrophysics Data System (ADS)

    Zambrano-Bigiarini, M.; Rojas, R.

    2012-04-01

    Particle Swarm Optimisation (PSO) is a recent and powerful population-based stochastic optimisation technique inspired by social behaviour of bird flocking, which shares similarities with other evolutionary techniques such as Genetic Algorithms (GA). In PSO, however, each individual of the population, known as particle in PSO terminology, adjusts its flying trajectory on the multi-dimensional search-space according to its own experience (best-known personal position) and the one of its neighbours in the swarm (best-known local position). PSO has recently received a surge of attention given its flexibility, ease of programming, low memory and CPU requirements, and efficiency. Despite these advantages, PSO may still get trapped into sub-optimal solutions, suffer from swarm explosion or premature convergence. Thus, the development of enhancements to the "canonical" PSO is an active area of research. To date, several modifications to the canonical PSO have been proposed in the literature, resulting into a large and dispersed collection of codes and algorithms which might well be used for similar if not identical purposes. In this work we present hydroPSO, a platform-independent R package implementing several enhancements to the canonical PSO that we consider of utmost importance to bring this technique to the attention of a broader community of scientists and practitioners. hydroPSO is model-independent, allowing the user to interface any model code with the calibration engine without having to invest considerable effort in customizing PSO to a new calibration problem. Some of the controlling options to fine-tune hydroPSO are: four alternative topologies, several types of inertia weight, time-variant acceleration coefficients, time-variant maximum velocity, regrouping of particles when premature convergence is detected, different types of boundary conditions and many others. Additionally, hydroPSO implements recent PSO variants such as: Improved Particle Swarm

  5. A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows

    PubMed Central

    Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian

    2015-01-01

    A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following. PMID:26343655

  6. Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm

    PubMed Central

    Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng

    2015-01-01

    Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm. PMID:25603158

  7. Parameter estimation of fractional-order chaotic systems by using quantum parallel particle swarm optimization algorithm.

    PubMed

    Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng

    2015-01-01

    Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm. PMID:25603158

  8. A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows.

    PubMed

    Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian

    2015-01-01

    A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following. PMID:26343655

  9. On the application of Particle Swarm Optimization strategies on Scholte-wave inversion

    NASA Astrophysics Data System (ADS)

    Wilken, D.; Rabbel, W.

    2012-07-01

    We investigate different aspects concerning the application of swarm intelligence optimization to the inversion of Scholte-wave phase-slowness frequency (p-f) spectra with respect to shear wave velocity structure. Besides human influence due to the dependence on a priori information for starting models and interpretation of p-f spectra as well as noise, the model resolution of the inversion problem is strongly influenced by the multimodality of the misfit function. We thus tested the efficiency of global, stochastic optimization approaches with focus on swarm intelligence methods that can explore the multiple minima of the misfit function. A comparison among different PSO schemes by applying them to synthetic Scholte-wave spectra led to a hybrid of Particle Swarm Optimization and Downhill Simplex providing the best resolution of inverted shear wave velocity depth models. The results showed a very low spread of best fitting solutions of 7 per cent in shear wave velocity and an average of 9 per cent for noisy synthetic data and a very good fit to the true synthetic model used for computation of the input data. To classify this method we also compared the probability of finding a good fit in synthetic spectra inversion with that of Evolutionary Algorithm, Simulated Annealing, Neighbourhood Algorithm and Artificial Bee Colony algorithm. Again the hybrid optimization scheme showed its predominance. The usage of stochastic algorithms furthermore allowed a new way of misfit definition in terms of dispersion curve slowness residuals making the inversion scheme independent on Scholte-wave mode identification and allowing joint inversion of fundamental mode and higher mode information. Finally we used the hybrid optimization scheme and the misfit calculation for the inversion of 2-D shear wave velocity profiles from two locations in the North- and Baltic Sea. The models show acceptable resolution and a very good structural correlation to high resolution reflection seismic

  10. Parameter Identification of Robot Manipulators: A Heuristic Particle Swarm Search Approach

    PubMed Central

    Yan, Danping; Lu, Yongzhong; Levy, David

    2015-01-01

    Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles’ local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators. PMID:26039090

  11. Finite element model updating approach to damage identification in beams using particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Saada, Mohamed M.; Arafa, Mustafa H.; Nassef, Ashraf O.

    2013-06-01

    The use of vibration-based techniques in damage identification has recently received considerable attention in many engineering disciplines. While various damage indicators have been proposed in the literature, those relying only on changes in the natural frequencies are quite appealing since these quantities can conveniently be acquired. Nevertheless, the use of natural frequencies in damage identification is faced with many obstacles, including insensitivity and non-uniqueness issues. The aim of this article is to develop a viable damage identification scheme based only on changes in the natural frequencies and to attempt to overcome the challenges typically encountered. The proposed methodology relies on building a finite element model (FEM) of the structure under investigation. An improved particle swarm optimization algorithm is proposed to facilitate updating the FEM in accordance with experimentally determined natural frequencies in order to predict the damage location and extent. The method is tested on beam structures and was shown to be an effective tool for damage identification.

  12. Infrared face recognition based on binary particle swarm optimization and SVM-wrapper model

    NASA Astrophysics Data System (ADS)

    Xie, Zhihua; Liu, Guodong

    2015-10-01

    Infrared facial imaging, being light- independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. Robust feature selection and representation is a key issue for infrared face recognition research. This paper proposes a novel infrared face recognition method based on local binary pattern (LBP). LBP can improve the robust of infrared face recognition under different environment situations. How to make full use of the discriminant ability in LBP patterns is an important problem. A search algorithm combination binary particle swarm with SVM is used to find out the best discriminative subset in LBP features. Experimental results show that the proposed method outperforms traditional LBP based infrared face recognition methods. It can significantly improve the recognition performance of infrared face recognition.

  13. Design Optimization of Pin Fin Geometry Using Particle Swarm Optimization Algorithm

    PubMed Central

    Hamadneh, Nawaf; Khan, Waqar A.; Sathasivam, Saratha; Ong, Hong Choon

    2013-01-01

    Particle swarm optimization (PSO) is employed to investigate the overall performance of a pin fin.The following study will examine the effect of governing parameters on overall thermal/fluid performance associated with different fin geometries, including, rectangular plate fins as well as square, circular, and elliptical pin fins. The idea of entropy generation minimization, EGM is employed to combine the effects of thermal resistance and pressure drop within the heat sink. A general dimensionless expression for the entropy generation rate is obtained by considering a control volume around the pin fin including base plate and applying the conservations equations for mass and energy with the entropy balance. Selected fin geometries are examined for the heat transfer, fluid friction, and the minimum entropy generation rate corresponding to different parameters including axis ratio, aspect ratio, and Reynolds number. The results clearly indicate that the preferred fin profile is very dependent on these parameters. PMID:23741525

  14. Application of Particle Swarm Optimization Algorithm for Better Nano-Devices

    NASA Astrophysics Data System (ADS)

    Singh, Nameirakpam Basanta; Deb, Sanjoy; Mishra, Guru P.; Sarkar, Samir Kumar; Sarkar, Subir Kumar

    Particle swarm optimization, an intelligent soft computing tool is employed to determine the optimized system parameters of GaAs quantum well for better high frequency performance under hot electron condition at room temperature. The energy loss through LO phonon and momentum loss through LO phonon, deformation acoustic phonon and ionized impurity (both background and remote) are incorporated in the present calculations. For a typical dc biasing field, it is possible to predict the optimum values of system parameters like lattice temperature, well width and two-dimensional carrier concentration for realizing a particular high frequency response characterised by well defined cut-off frequency. Such optimization will make feasible the fabrication of a variety of new quantum devices with desired characteristics.

  15. Energy and operation management of a microgrid using particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Radosavljević, Jordan; Jevtić, Miroljub; Klimenta, Dardan

    2016-05-01

    This article presents an efficient algorithm based on particle swarm optimization (PSO) for energy and operation management (EOM) of a microgrid including different distributed generation units and energy storage devices. The proposed approach employs PSO to minimize the total energy and operating cost of the microgrid via optimal adjustment of the control variables of the EOM, while satisfying various operating constraints. Owing to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties and market prices, a probabilistic approach in the EOM is introduced. The proposed method is examined and tested on a typical grid-connected microgrid including fuel cell, gas-fired microturbine, wind turbine, photovoltaic and energy storage devices. The obtained results prove the efficiency of the proposed approach to solve the EOM of the microgrids.

  16. Particle swarm optimization and its application in MEG source localization using single time sliced data

    NASA Astrophysics Data System (ADS)

    Lin, Juan; Liu, Chenglian; Guo, Yongning

    2014-10-01

    The estimation of neural active sources from the magnetoencephalography (MEG) data is a very critical issue for both clinical neurology and brain functions research. A widely accepted source-modeling technique for MEG involves calculating a set of equivalent current dipoles (ECDs). Depth in the brain is one of difficulties in MEG source localization. Particle swarm optimization(PSO) is widely used to solve various optimization problems. In this paper we discuss its ability and robustness to find the global optimum in different depths of the brain when using single equivalent current dipole (sECD) model and single time sliced data. The results show that PSO is an effective global optimization to MEG source localization when given one dipole in different depths.

  17. Uniformly spaced 3D modeling of human face from two images using parallel particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Chang, Yau-Zen; Hou, Jung-Fu; Tsao, Yi Hsiang; Lee, Shih-Tseng

    2011-09-01

    This paper proposes a scheme for finding the correspondence between uniformly spaced locations on the images of human face captured from different viewpoints at the same instant. The correspondence is dedicated for 3D reconstruction to be used in the registration procedure for neurosurgery where the exposure to projectors must be seriously restricted. The approach utilizes structured light to enhance patterns on the images and is initialized with the scale-invariant feature transform (SIFT). Successive locations are found according to spatial order using a parallel version of the particle swarm optimization algorithm. Furthermore, false locations are singled out for correction by searching for outliers from fitted curves. Case studies show that the scheme is able to correctly generate 456 evenly spaced 3D coordinate points in 23 seconds from a single shot of projected human face using a PC with 2.66 GHz Intel Q9400 CPU and 4GB RAM.

  18. Application of quantum-behaved particle swarm optimization to motor imagery EEG classification.

    PubMed

    Hsu, Wei-Yen

    2013-12-01

    In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications. PMID:24156669

  19. Multi-terminal pipe routing by Steiner minimal tree and particle swarm optimisation

    NASA Astrophysics Data System (ADS)

    Liu, Qiang; Wang, Chengen

    2012-08-01

    Computer-aided design of pipe routing is of fundamental importance for complex equipments' developments. In this article, non-rectilinear branch pipe routing with multiple terminals that can be formulated as a Euclidean Steiner Minimal Tree with Obstacles (ESMTO) problem is studied in the context of an aeroengine-integrated design engineering. Unlike the traditional methods that connect pipe terminals sequentially, this article presents a new branch pipe routing algorithm based on the Steiner tree theory. The article begins with a new algorithm for solving the ESMTO problem by using particle swarm optimisation (PSO), and then extends the method to the surface cases by using geodesics to meet the requirements of routing non-rectilinear pipes on the surfaces of aeroengines. Subsequently, the adaptive region strategy and the basic visibility graph method are adopted to increase the computation efficiency. Numeral computations show that the proposed routing algorithm can find satisfactory routing layouts while running in polynomial time.

  20. Estimating stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Zhang, Chuan-Xin; Yuan, Yuan; Zhang, Hao-Wei; Shuai, Yong; Tan, He-Ping

    2016-09-01

    Considering features of stellar spectral radiation and sky surveys, we established a computational model for stellar effective temperatures, detected angular parameters and gray rates. Using known stellar flux data in some bands, we estimated stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization (SPSO). We first verified the reliability of SPSO, and then determined reasonable parameters that produced highly accurate estimates under certain gray deviation levels. Finally, we calculated 177 860 stellar effective temperatures and detected angular parameters using data from the Midcourse Space Experiment (MSX) catalog. These derived stellar effective temperatures were accurate when we compared them to known values from literatures. This research makes full use of catalog data and presents an original technique for studying stellar characteristics. It proposes a novel method for calculating stellar effective temperatures and detecting angular parameters, and provides theoretical and practical data for finding information about radiation in any band.

  1. Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system

    NASA Astrophysics Data System (ADS)

    Wu, Qi

    2010-03-01

    Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWv-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOv-SVM and other traditional methods.

  2. Fitting of interatomic potentials without forces: A parallel particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    González, Diego; Davis, Sergio

    2014-12-01

    We present a methodology for fitting interatomic potentials to ab initio data, using the particle swarm optimization (PSO) algorithm, needing only a set of positions and energies as input. The prediction error of energies associated with the fitted parameters can be close to 1 meV/atom or lower, for reference energies having a standard deviation of about 0.5 eV/atom. We tested our method by fitting a Sutton-Chen potential for copper from ab initio data, which is able to recover structural and dynamical properties, and obtain a better agreement of the predicted melting point versus the experimental value, as compared to the prediction of the standard Sutton-Chen parameters.

  3. Double global optimum genetic algorithm-particle swarm optimization-based welding robot path planning

    NASA Astrophysics Data System (ADS)

    Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng

    2016-02-01

    Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm-particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.

  4. Multiple R&D Projects Scheduling Optimization with Improved Particle Swarm Algorithm

    PubMed Central

    Liu, Mengqi; Shan, Miyuan; Wu, Juan

    2014-01-01

    For most enterprises, in order to win the initiative in the fierce competition of market, a key step is to improve their R&D ability to meet the various demands of customers more timely and less costly. This paper discusses the features of multiple R&D environments in large make-to-order enterprises under constrained human resource and budget, and puts forward a multi-project scheduling model during a certain period. Furthermore, we make some improvements to existed particle swarm algorithm and apply the one developed here to the resource-constrained multi-project scheduling model for a simulation experiment. Simultaneously, the feasibility of model and the validity of algorithm are proved in the experiment. PMID:25032232

  5. Weighted Fuzzy Interpolative Reasoning Based on the Slopes of Fuzzy Sets and Particle Swarm Optimization Techniques.

    PubMed

    Chen, Shyi-Ming; Hsin, Wen-Chyuan

    2015-07-01

    In this paper, we propose a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on the slopes of fuzzy sets. We also propose a particle swarm optimization (PSO)-based weights-learning algorithm to automatically learn the optimal weights of the antecedent variables of fuzzy rules for weighted fuzzy interpolative reasoning. We apply the proposed weighted fuzzy interpolative reasoning method using the proposed PSO-based weights-learning algorithm to deal with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems. The experimental results show that the proposed weighted fuzzy interpolative reasoning method using the proposed PSO-based weights-learning algorithm outperforms the existing methods for dealing with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems. PMID:25204003

  6. Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration

    NASA Astrophysics Data System (ADS)

    Petković, Dalibor; Gocic, Milan; Shamshirband, Shahaboddin; Qasem, Sultan Noman; Trajkovic, Slavisa

    2016-08-01

    Accurate estimation of the reference evapotranspiration (ET0) is important for the water resource planning and scheduling of irrigation systems. For this purpose, the radial basis function network with particle swarm optimization (RBFN-PSO) and radial basis function network with back propagation (RBFN-BP) were used in this investigation. The FAO-56 Penman-Monteith equation was used as reference equation to estimate ET0 for Serbia during the period of 1980-2010. The obtained simulation results confirmed the proposed models and were analyzed using the root mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination ( R 2). The analysis showed that the RBFN-PSO had better statistical characteristics than RBFN-BP and can be helpful for the ET0 estimation.

  7. DOA estimation for local scattered CDMA signals by particle swarm optimization.

    PubMed

    Chang, Jhih-Chung

    2012-01-01

    This paper deals with the direction-of-arrival (DOA) estimation of local scattered code-division multiple access (CDMA) signals based on a particle swarm optimization (PSO) search. For conventional spectral searching estimators with local scattering, the searching complexity and estimating accuracy strictly depend on the number of search grids used during the search. In order to obtain high-resolution and accurate DOA estimation, a smaller grid size is needed. This is time consuming and it is unclear how to determine the required number of search grids. In this paper, a modified PSO is presented to reduce the required search grids for the conventional spectral searching estimator with the effects of local scattering. Finally, several computer simulations are provided for illustration and comparison. PMID:22737004

  8. Extended particle swarm optimisation method for folding protein on triangular lattice.

    PubMed

    Guo, Yuzhen; Wu, Zikai; Wang, Ying; Wang, Yong

    2016-02-01

    In this study, the authors studied the protein structure prediction problem by the two-dimensional hydrophobic-polar model on triangular lattice. Particularly the non-compact conformation was modelled to fold the amino acid sequence into a relatively larger triangular lattice, which is more biologically realistic and significant than the compact conformation. Then protein structure prediction problem was abstracted to match amino acids to lattice points. Mathematically, the problem was formulated as an integer programming and they transformed the biological problem into an optimisation problem. To solve this problem, classical particle swarm optimisation algorithm was extended by the single point adjustment strategy. Compared with square lattice, conformations on triangular lattice are more flexible in several benchmark examples. They further compared the authors' algorithm with hybrid of hill climbing and genetic algorithm. The results showed that their method was more effective in finding solution with lower energy and less running time. PMID:26816397

  9. Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration

    NASA Astrophysics Data System (ADS)

    Petković, Dalibor; Gocic, Milan; Shamshirband, Shahaboddin; Qasem, Sultan Noman; Trajkovic, Slavisa

    2015-06-01

    Accurate estimation of the reference evapotranspiration (ET0) is important for the water resource planning and scheduling of irrigation systems. For this purpose, the radial basis function network with particle swarm optimization (RBFN-PSO) and radial basis function network with back propagation (RBFN-BP) were used in this investigation. The FAO-56 Penman-Monteith equation was used as reference equation to estimate ET0 for Serbia during the period of 1980-2010. The obtained simulation results confirmed the proposed models and were analyzed using the root mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R 2). The analysis showed that the RBFN-PSO had better statistical characteristics than RBFN-BP and can be helpful for the ET0 estimation.

  10. Joint inversion of TEM and DC in roadway advanced detection based on particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Cheng, Jiulong; Li, Fei; Peng, Suping; Sun, Xiaoyun; Zheng, Jing; Jia, Jizhe

    2015-12-01

    Transient electromagnetic method (TEM)and direct current method (DC)are two key widely applied methods for practical roadway detection, but both have their limitations. To take the advantage of each method, a synchronous nonlinear joint inversion method is proposed based on TEM and DC by using particle swarm optimization (PSO)algorithm. Firstly, a model with double low resistance anomaly and interference is constructed to test the performance of the method. Then the independent inversion and joint inversion are calculated by using the model built above. It is demonstrated that the joint inversion helped in improving the interpretation of the data to get better results. It is because that the suppression of interference and separation of the resistivity anomalies ahead and the back of the roadway working face using the proposed method. Finally, the proposed method was successfully used in a coalmine in Huainan coalfield in east China to demonstrate its practical usefulness.

  11. Solving initial and boundary value problems using learning automata particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Nemati, Kourosh; Mariyam Shamsuddin, Siti; Darus, Maslina

    2015-05-01

    In this article, the particle swarm optimization (PSO) algorithm is modified to use the learning automata (LA) technique for solving initial and boundary value problems. A constrained problem is converted into an unconstrained problem using a penalty method to define an appropriate fitness function, which is optimized using the LA-PSO method. This method analyses a large number of candidate solutions of the unconstrained problem with the LA-PSO algorithm to minimize an error measure, which quantifies how well a candidate solution satisfies the governing ordinary differential equations (ODEs) or partial differential equations (PDEs) and the boundary conditions. This approach is very capable of solving linear and nonlinear ODEs, systems of ordinary differential equations, and linear and nonlinear PDEs. The computational efficiency and accuracy of the PSO algorithm combined with the LA technique for solving initial and boundary value problems were improved. Numerical results demonstrate the high accuracy and efficiency of the proposed method.

  12. Improvements on particle swarm optimization algorithm for velocity calibration in microseismic monitoring

    NASA Astrophysics Data System (ADS)

    Yang, Yue; Wen, Jian; Chen, Xiaofei

    2015-07-01

    In this paper, we apply particle swarm optimization (PSO), an artificial intelligence technique, to velocity calibration in microseismic monitoring. We ran simulations with four 1-D layered velocity models and three different initial model ranges. The results using the basic PSO algorithm were reliable and accurate for simple models, but unsuccessful for complex models. We propose the staged shrinkage strategy (SSS) for the PSO algorithm. The SSS-PSO algorithm produced robust inversion results and had a fast convergence rate. We investigated the effects of PSO's velocity clamping factor in terms of the algorithm reliability and computational efficiency. The velocity clamping factor had little impact on the reliability and efficiency of basic PSO, whereas it had a large effect on the efficiency of SSS-PSO. Reassuringly, SSS-PSO exhibits marginal reliability fluctuations, which suggests that it can be confidently implemented.

  13. Enhanced fuzzy-connective-based hierarchical aggregation network using particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Wang, Fang-Fang; Su, Chao-Ton

    2014-11-01

    The fuzzy-connective-based aggregation network is similar to the human decision-making process. It is capable of aggregating and propagating degrees of satisfaction of a set of criteria in a hierarchical manner. Its interpreting ability and transparency make it especially desirable. To enhance its effectiveness and further applicability, a learning approach is successfully developed based on particle swarm optimization to determine the weights and parameters of the connectives in the network. By experimenting on eight datasets with different characteristics and conducting further statistical tests, it has been found to outperform the gradient- and genetic algorithm-based learning approaches proposed in the literature; furthermore, it is capable of generating more accurate estimates. The present approach retains the original benefits of fuzzy-connective-based aggregation networks and is widely applicable. The characteristics of the learning approaches are also discussed and summarized, providing better understanding of the similarities and differences among these three approaches.

  14. Design optimization of pin fin geometry using particle swarm optimization algorithm.

    PubMed

    Hamadneh, Nawaf; Khan, Waqar A; Sathasivam, Saratha; Ong, Hong Choon

    2013-01-01

    Particle swarm optimization (PSO) is employed to investigate the overall performance of a pin fin.The following study will examine the effect of governing parameters on overall thermal/fluid performance associated with different fin geometries, including, rectangular plate fins as well as square, circular, and elliptical pin fins. The idea of entropy generation minimization, EGM is employed to combine the effects of thermal resistance and pressure drop within the heat sink. A general dimensionless expression for the entropy generation rate is obtained by considering a control volume around the pin fin including base plate and applying the conservations equations for mass and energy with the entropy balance. Selected fin geometries are examined for the heat transfer, fluid friction, and the minimum entropy generation rate corresponding to different parameters including axis ratio, aspect ratio, and Reynolds number. The results clearly indicate that the preferred fin profile is very dependent on these parameters. PMID:23741525

  15. A particle swarm optimization variant with an inner variable learning strategy.

    PubMed

    Wu, Guohua; Pedrycz, Witold; Ma, Manhao; Qiu, Dishan; Li, Haifeng; Liu, Jin

    2014-01-01

    Although Particle Swarm Optimization (PSO) has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL) is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL) strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge. PMID:24587746

  16. Mutation particle swarm optimization of the BP-PID controller for piezoelectric ceramics

    NASA Astrophysics Data System (ADS)

    Zheng, Huaqing; Jiang, Minlan

    2016-01-01

    PID control is the most common used method in industrial control because its structure is simple and it is easy to implement. PID controller has good control effect, now it has been widely used. However, PID method has a few limitations. The overshoot of the PID controller is very big. The adjustment time is long. When the parameters of controlled plant are changing over time, the parameters of controller could hardly change automatically to adjust to changing environment. Thus, it can't meet the demand of control quality in the process of controlling piezoelectric ceramic. In order to effectively control the piezoelectric ceramic and improve the control accuracy, this paper replaced the learning algorithm of the BP with the mutation particle swarm optimization algorithm(MPSO) on the process of the parameters setting of BP-PID. That designed a better self-adaptive controller which is combing the BP neural network based on mutation particle swarm optimization with the conventional PID control theory. This combination is called the MPSO-BP-PID. In the mechanism of the MPSO, the mutation operation is carried out with the fitness variance and the global best fitness value as the standard. That can overcome the precocious of the PSO and strengthen its global search ability. As a result, the MPSO-BP-PID can complete controlling the controlled plant with higher speed and accuracy. Therefore, the MPSO-BP-PID is applied to the piezoelectric ceramic. It can effectively overcome the hysteresis, nonlinearity of the piezoelectric ceramic. In the experiment, compared with BP-PID and PSO-BP-PID, it proved that MPSO is effective and the MPSO-BP-PID has stronger adaptability and robustness.

  17. Particle Swarm Optimization for inverse modeling of solute transport in fractured gneiss aquifer

    NASA Astrophysics Data System (ADS)

    Abdelaziz, Ramadan; Zambrano-Bigiarini, Mauricio

    2014-08-01

    Particle Swarm Optimization (PSO) has received considerable attention as a global optimization technique from scientists of different disciplines around the world. In this article, we illustrate how to use PSO for inverse modeling of a coupled flow and transport groundwater model (MODFLOW2005-MT3DMS) in a fractured gneiss aquifer. In particular, the hydroPSO R package is used as optimization engine, because it has been specifically designed to calibrate environmental, hydrological and hydrogeological models. In addition, hydroPSO implements the latest Standard Particle Swarm Optimization algorithm (SPSO-2011), with an adaptive random topology and rotational invariance constituting the main advancements over previous PSO versions. A tracer test conducted in the experimental field at TU Bergakademie Freiberg (Germany) is used as case study. A double-porosity approach is used to simulate the solute transport in the fractured Gneiss aquifer. Tracer concentrations obtained with hydroPSO were in good agreement with its corresponding observations, as measured by a high value of the coefficient of determination and a low sum of squared residuals. Several graphical outputs automatically generated by hydroPSO provided useful insights to assess the quality of the calibration results. It was found that hydroPSO required a small number of model runs to reach the region of the global optimum, and it proved to be both an effective and efficient optimization technique to calibrate the movement of solute transport over time in a fractured aquifer. In addition, the parallel feature of hydroPSO allowed to reduce the total computation time used in the inverse modeling process up to an eighth of the total time required without using that feature. This work provides a first attempt to demonstrate the capability and versatility of hydroPSO to work as an optimizer of a coupled flow and transport model for contaminant migration.

  18. Particle Swarm Optimization for inverse modeling of solute transport in fractured gneiss aquifer.

    PubMed

    Abdelaziz, Ramadan; Zambrano-Bigiarini, Mauricio

    2014-08-01

    Particle Swarm Optimization (PSO) has received considerable attention as a global optimization technique from scientists of different disciplines around the world. In this article, we illustrate how to use PSO for inverse modeling of a coupled flow and transport groundwater model (MODFLOW2005-MT3DMS) in a fractured gneiss aquifer. In particular, the hydroPSO R package is used as optimization engine, because it has been specifically designed to calibrate environmental, hydrological and hydrogeological models. In addition, hydroPSO implements the latest Standard Particle Swarm Optimization algorithm (SPSO-2011), with an adaptive random topology and rotational invariance constituting the main advancements over previous PSO versions. A tracer test conducted in the experimental field at TU Bergakademie Freiberg (Germany) is used as case study. A double-porosity approach is used to simulate the solute transport in the fractured Gneiss aquifer. Tracer concentrations obtained with hydroPSO were in good agreement with its corresponding observations, as measured by a high value of the coefficient of determination and a low sum of squared residuals. Several graphical outputs automatically generated by hydroPSO provided useful insights to assess the quality of the calibration results. It was found that hydroPSO required a small number of model runs to reach the region of the global optimum, and it proved to be both an effective and efficient optimization technique to calibrate the movement of solute transport over time in a fractured aquifer. In addition, the parallel feature of hydroPSO allowed to reduce the total computation time used in the inverse modeling process up to an eighth of the total time required without using that feature. This work provides a first attempt to demonstrate the capability and versatility of hydroPSO to work as an optimizer of a coupled flow and transport model for contaminant migration. PMID:25035936

  19. Discrete particle swarm optimization for identifying community structures in signed social networks.

    PubMed

    Cai, Qing; Gong, Maoguo; Shen, Bo; Ma, Lijia; Jiao, Licheng

    2014-10-01

    Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. PMID:24856248

  20. On the performance of linear decreasing inertia weight particle swarm optimization for global optimization.

    PubMed

    Arasomwan, Martins Akugbe; Adewumi, Aderemi Oluyinka

    2013-01-01

    Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted. PMID:24324383

  1. Computational methods of Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier on economic dispatch issues (case study on electrical system of Java-Bali IV area)

    NASA Astrophysics Data System (ADS)

    Komsiyah, S.

    2014-03-01

    The objective in this paper is about economic dispatch problem of electric power generation where scheduling the committed generating units outputs so as to meet the required load demand at minimum operating cost, while satisfying all units and system equality and inequality constraint. In the operating of electric power system, an economic planning problem is one of variables that its must be considered since economically planning will give more efficiency in operational cost. In this paper the economic dispatch problem which has non linear cost function solved by using swarm intelligent method is Gaussian Particle Swarm Optimization (GPSO) and Lagrange Multiplier. GPSO is a population-based stochastic algorithms which their moving inspired by swarm intelligent and probabilities theories. To analize its accuracy, the economic dispatch solution by GPSO method will be compared with Lagrange multiplier method. From the running test result the GPSO method give economically planning calculation which it better than Lagrange multiplier method and the GPSO method faster to getting error convergence. Therefore the GPSO method have better performance to getting global best solution than the Lagrange method.

  2. Objective comparison of particle tracking methods.

    PubMed

    Chenouard, Nicolas; Smal, Ihor; de Chaumont, Fabrice; Maška, Martin; Sbalzarini, Ivo F; Gong, Yuanhao; Cardinale, Janick; Carthel, Craig; Coraluppi, Stefano; Winter, Mark; Cohen, Andrew R; Godinez, William J; Rohr, Karl; Kalaidzidis, Yannis; Liang, Liang; Duncan, James; Shen, Hongying; Xu, Yingke; Magnusson, Klas E G; Jaldén, Joakim; Blau, Helen M; Paul-Gilloteaux, Perrine; Roudot, Philippe; Kervrann, Charles; Waharte, François; Tinevez, Jean-Yves; Shorte, Spencer L; Willemse, Joost; Celler, Katherine; van Wezel, Gilles P; Dan, Han-Wei; Tsai, Yuh-Show; Ortiz de Solórzano, Carlos; Olivo-Marin, Jean-Christophe; Meijering, Erik

    2014-03-01

    Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers. PMID:24441936

  3. Objective comparison of particle tracking methods

    PubMed Central

    Chenouard, Nicolas; Smal, Ihor; de Chaumont, Fabrice; Maška, Martin; Sbalzarini, Ivo F.; Gong, Yuanhao; Cardinale, Janick; Carthel, Craig; Coraluppi, Stefano; Winter, Mark; Cohen, Andrew R.; Godinez, William J.; Rohr, Karl; Kalaidzidis, Yannis; Liang, Liang; Duncan, James; Shen, Hongying; Xu, Yingke; Magnusson, Klas E. G.; Jaldén, Joakim; Blau, Helen M.; Paul-Gilloteaux, Perrine; Roudot, Philippe; Kervrann, Charles; Waharte, François; Tinevez, Jean-Yves; Shorte, Spencer L.; Willemse, Joost; Celler, Katherine; van Wezel, Gilles P.; Dan, Han-Wei; Tsai, Yuh-Show; de Solórzano, Carlos Ortiz; Olivo-Marin, Jean-Christophe; Meijering, Erik

    2014-01-01

    Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Since manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized, for the first time, an open competition, in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to important practical conclusions for users and developers. PMID:24441936

  4. Optimization of the Infrastructure of Reinforced Concrete Reservoirs by a Particle Swarm Algorithm

    NASA Astrophysics Data System (ADS)

    Kia, Saeed; Sebt, Mohammad Hassan; Shahhosseini, Vahid

    2015-03-01

    Optimization techniques may be effective in finding the best modeling and shapes for reinforced concrete reservoirs (RCR) to improve their durability and mechanical behavior, particularly for avoiding or reducing the bending moments in these structures. RCRs are one of the major structures applied for reserving fluids to be used in drinking water networks. Usually, these structures have fixed shapes which are designed and calculated based on input discharges, the conditions of the structure's topology, and geotechnical locations with various combinations of static and dynamic loads. In this research, the elements of reservoir walls are first typed according to the performance analyzed; then the range of the membrane based on the thickness and the minimum and maximum cross sections of the bar used are determined in each element. This is done by considering the variable constraints, which are estimated by the maximum stress capacity. In the next phase, based on the reservoir analysis and using the algorithm of the PARIS connector, the related information is combined with the code for the PSO algorithm, i.e., an algorithm for a swarming search, to determine the optimum thickness of the cross sections for the reservoir membrane's elements and the optimum cross section of the bar used. Based on very complex mathematical linear models for the correct embedding and angles related to achain of peripheral strengthening membranes, which optimize the vibration of the structure, a mutual relation is selected between the modeling software and the code for a particle swarm optimization algorithm. Finally, the comparative weight of the concrete reservoir optimized by the peripheral strengthening membrane is analyzed using common methods. This analysis shows a 19% decrease in the bar's weight, a 20% decrease in the concrete's weight, and a minimum 13% saving in construction costs according to the items of a checklist for a concrete reservoir at 10,000 m3.

  5. Designing a Multistage Supply Chain in Cross-Stage Reverse Logistics Environments: Application of Particle Swarm Optimization Algorithms

    PubMed Central

    Chiang, Tzu-An; Che, Z. H.

    2014-01-01

    This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), VMax method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did. PMID:24772026

  6. Multi-Item Multiperiodic Inventory Control Problem with Variable Demand and Discounts: A Particle Swarm Optimization Algorithm

    PubMed Central

    Mousavi, Seyed Mohsen; Niaki, S. T. A.; Bahreininejad, Ardeshir; Musa, Siti Nurmaya

    2014-01-01

    A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA. PMID:25093195

  7. Designing a multistage supply chain in cross-stage reverse logistics environments: application of particle swarm optimization algorithms.

    PubMed

    Chiang, Tzu-An; Che, Z H; Cui, Zhihua

    2014-01-01

    This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), V(Max) method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did. PMID:24772026

  8. Conformational sampling enhancement of replica exchange molecular dynamics simulations using swarm particle intelligence

    NASA Astrophysics Data System (ADS)

    Kamberaj, Hiqmet

    2015-09-01

    In this paper, we present a new method based on swarm particle social intelligence for use in replica exchange molecular dynamics simulations. In this method, the replicas (representing the different system configurations) are allowed communicating with each other through the individual and social knowledge, in additional to considering them as a collection of real particles interacting through the Newtonian forces. The new method is based on the modification of the equations of motion in such way that the replicas are driven towards the global energy minimum. The method was tested for the Lennard-Jones clusters of N = 4, 5, and 6 atoms. Our results showed that the new method is more efficient than the conventional replica exchange method under the same practical conditions. In particular, the new method performed better on optimizing the distribution of the replicas among the thermostats with time and, in addition, ergodic convergence is observed to be faster. We also introduce a weighted histogram analysis method allowing analyzing the data from simulations by combining data from all of the replicas and rigorously removing the inserted bias.

  9. The contribution of particle swarm optimization to three-dimensional slope stability analysis.

    PubMed

    Kalatehjari, Roohollah; Rashid, Ahmad Safuan A; Ali, Nazri; Hajihassani, Mohsen

    2014-01-01

    Over the last few years, particle swarm optimization (PSO) has been extensively applied in various geotechnical engineering including slope stability analysis. However, this contribution was limited to two-dimensional (2D) slope stability analysis. This paper applied PSO in three-dimensional (3D) slope stability problem to determine the critical slip surface (CSS) of soil slopes. A detailed description of adopted PSO was presented to provide a good basis for more contribution of this technique to the field of 3D slope stability problems. A general rotating ellipsoid shape was introduced as the specific particle for 3D slope stability analysis. A detailed sensitivity analysis was designed and performed to find the optimum values of parameters of PSO. Example problems were used to evaluate the applicability of PSO in determining the CSS of 3D slopes. The first example presented a comparison between the results of PSO and PLAXI-3D finite element software and the second example compared the ability of PSO to determine the CSS of 3D slopes with other optimization methods from the literature. The results demonstrated the efficiency and effectiveness of PSO in determining the CSS of 3D soil slopes. PMID:24991652

  10. The Contribution of Particle Swarm Optimization to Three-Dimensional Slope Stability Analysis

    PubMed Central

    A Rashid, Ahmad Safuan; Ali, Nazri

    2014-01-01

    Over the last few years, particle swarm optimization (PSO) has been extensively applied in various geotechnical engineering including slope stability analysis. However, this contribution was limited to two-dimensional (2D) slope stability analysis. This paper applied PSO in three-dimensional (3D) slope stability problem to determine the critical slip surface (CSS) of soil slopes. A detailed description of adopted PSO was presented to provide a good basis for more contribution of this technique to the field of 3D slope stability problems. A general rotating ellipsoid shape was introduced as the specific particle for 3D slope stability analysis. A detailed sensitivity analysis was designed and performed to find the optimum values of parameters of PSO. Example problems were used to evaluate the applicability of PSO in determining the CSS of 3D slopes. The first example presented a comparison between the results of PSO and PLAXI-3D finite element software and the second example compared the ability of PSO to determine the CSS of 3D slopes with other optimization methods from the literature. The results demonstrated the efficiency and effectiveness of PSO in determining the CSS of 3D soil slopes. PMID:24991652

  11. Conformational sampling enhancement of replica exchange molecular dynamics simulations using swarm particle intelligence

    SciTech Connect

    Kamberaj, Hiqmet

    2015-09-28

    In this paper, we present a new method based on swarm particle social intelligence for use in replica exchange molecular dynamics simulations. In this method, the replicas (representing the different system configurations) are allowed communicating with each other through the individual and social knowledge, in additional to considering them as a collection of real particles interacting through the Newtonian forces. The new method is based on the modification of the equations of motion in such way that the replicas are driven towards the global energy minimum. The method was tested for the Lennard-Jones clusters of N = 4,  5, and 6 atoms. Our results showed that the new method is more efficient than the conventional replica exchange method under the same practical conditions. In particular, the new method performed better on optimizing the distribution of the replicas among the thermostats with time and, in addition, ergodic convergence is observed to be faster. We also introduce a weighted histogram analysis method allowing analyzing the data from simulations by combining data from all of the replicas and rigorously removing the inserted bias.

  12. Bayesian network structure learning based on the chaotic particle swarm optimization algorithm.

    PubMed

    Zhang, Q; Li, Z; Zhou, C J; Wei, X P

    2013-01-01

    The Bayesian network (BN) is a knowledge representation form, which has been proven to be valuable in the gene regulatory network reconstruction because of its capability of capturing causal relationships between genes. Learning BN structures from a database is a nondeterministic polynomial time (NP)-hard problem that remains one of the most exciting challenges in machine learning. Several heuristic searching techniques have been used to find better network structures. Among these algorithms, the classical K2 algorithm is the most successful. Nonetheless, the performance of the K2 algorithm is greatly affected by a prior ordering of input nodes. The proposed method in this paper is based on the chaotic particle swarm optimization (CPSO) and the K2 algorithm. Because the PSO algorithm completely entraps the local minimum in later evolutions, we combined the PSO algorithm with the chaos theory, which has the properties of ergodicity, randomness, and regularity. Experimental results show that the proposed method can improve the convergence rate of particles and identify networks more efficiently and accurately. PMID:24222226

  13. Particle swarm optimization of the stable structure of tetrahexahedral Pt-based bimetallic nanoparticles

    NASA Astrophysics Data System (ADS)

    Liu, Tun-Dong; Fan, Tian-E.; Shao, Gui-Fang; Zheng, Ji-Wen; Wen, Yu-Hua

    2014-08-01

    Bimetallic nanoparticles, enclosed by high-index facets, have great catalytic activity and selectivity owing to the synergy effects of high-index facets and the electronic structures of alloy. In this paper, a discrete particle swarm optimization algorithm was employed to systematically investigate the structural stability and features of tetrahexahedral Pt-based bimetallic nanoparticles with high-index facets. Different Pt/Ag, Pt/Cu, Pt/Pd atom ratios and particle sizes were considered in this work. The simulation results reveal that these alloy nanoparticles exhibit considerably different structural characteristics. Pt-Ag nanoparticles tend to form Pt-Ag core-shell structure. Pt-Cu nanoparticles are preferred to take multi-shell structure with Cu on the outer surface while Pt-Pd nanoparticles present a mixing structure in the interior and Pd-dominated surface. Atomic distribution and bonding characteristics were applied to further characterize the structural features of Pt-based nanoparticles. This study provides an important insight into the structural stability and features of Pt-based nanoparticles with different alloys.

  14. Integrative modeling and novel particle swarm-based optimal design of wind farms

    NASA Astrophysics Data System (ADS)

    Chowdhury, Souma

    To meet the energy needs of the future, while seeking to decrease our carbon footprint, a greater penetration of sustainable energy resources such as wind energy is necessary. However, a consistent growth of wind energy (especially in the wake of unfortunate policy changes and reported under-performance of existing projects) calls for a paradigm shift in wind power generation technologies. This dissertation develops a comprehensive methodology to explore, analyze and define the interactions between the key elements of wind farm development, and establish the foundation for designing high-performing wind farms. The primary contribution of this research is the effective quantification of the complex combined influence of wind turbine features, turbine placement, farm-land configuration, nameplate capacity, and wind resource variations on the energy output of the wind farm. A new Particle Swarm Optimization (PSO) algorithm, uniquely capable of preserving population diversity while addressing discrete variables, is also developed to provide powerful solutions towards optimizing wind farm configurations. In conventional wind farm design, the major elements that influence the farm performance are often addressed individually. The failure to fully capture the critical interactions among these factors introduces important inaccuracies in the projected farm performance and leads to suboptimal wind farm planning. In this dissertation, we develop the Unrestricted Wind Farm Layout Optimization (UWFLO) methodology to model and optimize the performance of wind farms. The UWFLO method obviates traditional assumptions regarding (i) turbine placement, (ii) turbine-wind flow interactions, (iii) variation of wind conditions, and (iv) types of turbines (single/multiple) to be installed. The allowance of multiple turbines, which demands complex modeling, is rare in the existing literature. The UWFLO method also significantly advances the state of the art in wind farm optimization by

  15. Electron Swarms

    NASA Astrophysics Data System (ADS)

    Crompton, Robert W.

    1998-10-01

    Swarm experiments provide an invaluable link between gaseous electronics and atomic physics, that is, between the collective behavior of electrons in gases in electric and magnetic fields and the collision processes that determine that behavior. Early swarm experiments were made to gain an understanding of the basic physics of electrical conduction in gases and electrical breakdown. Subsequent peaks of activity have been associated with attempts to explain quantitatively electromagnetic wave propagation in the ionosphere and in high temperature air, and with applied research in such diverse areas as gas lasers, health physics, gas insulation for high voltage transmission lines, plasma processing, and particle detectors . Through improved experimental techniques and the application of numerical techniques to unravel the complex connection between the individual electron-neutral collisions and the transport coefficients that characterize the properties of the swarm, swarm experiments now contribute accurate, and sometimes unique, cross section data for low-energy electron-atom/molecule collisions. Alternatively they can provide self-consistent sets of cross sections that enable reliable forecasts of the collective behaviour to be made. In the talk I shall aim to provide an understanding of the basic principles underlying swarm experiments, and the interpratation of the results from them, through a description of their development and application up to the present day.

  16. NanoSWARM: A Nano-satellite Mission to Measure Particles and Fields Around the Moon

    NASA Astrophysics Data System (ADS)

    Garrick-Bethell, I.

    2015-12-01

    The NanoSWARM mission concept uses a fleet of cubesats around the Moon to address a number of open problems in planetary science: 1) The mechanisms of space weathering, 2) The origins of planetary magnetism, 3) The origins, distributions, and migration processes of surface water on airless bodies, and 4) The physics of small-scale magnetospheres. To accomplish these goals, NanoSWARM targets scientifically rich features on the Moon known as swirls. Swirls are high-albedo features correlated with strong magnetic fields and low surface-water. NanoSWARM cubesats will make the first near-surface (<1 km altitude) measurements of solar wind flux and magnetic fields at swirls. NanoSWARM cubesats will also perform low-altitude neutron measurements to provide key constraints on the distribution of polar hydrogen concentrations, which are important volatile sinks in the lunar water cycle. To release its cubesats, NanoSWARM uses a high-heritage mother ship in a low altitude, polar, circular orbit. NanoSWARM's results will have direct applications to the geophysics, volatile distribution, and plasma physics of numerous other bodies, in particular asteroids and the terrestrial planets. The technologies and methods used by NanoSWARM will enable many new cubesat missions in the next decade. NanoSWARM was proposed as a NASA Discovery mission in February 2015.

  17. NanoSWARM - A nano-satellite mission to measure particles and fields around the Moon

    NASA Astrophysics Data System (ADS)

    Garrick-Bethell, Ian; Russell, Christopher; Pieters, Carle; Weiss, Benjamin; Halekas, Jasper; Poppe, Andrew; Larson, Davin; Lawrence, David; Elphic, Richard; Hayne, Paul; Blakely, Richard; Kim, Khan-Hyuk; Choi, Young-Jun; Jin, Ho; Hemingway, Doug; Nayak, Michael; Puig-Suari, Jordi; Jaroux, Belgacem; Warwick, Steven

    2015-04-01

    The NanoSWARM mission concept uses a fleet of cubesats around the Moon to address a number of open problems in planetary science: 1) The mechanisms of space weathering, 2) The origins of planetary magnetism, 3) The origins, distributions, and migration processes of surface water on airless bodies, and 4) The physics of small-scale magnetospheres. To accomplish these goals, NanoSWARM targets scientifically rich features on the Moon known as swirls. Swirls are high-albedo features correlated with strong magnetic fields and low surface-water. NanoSWARM cubesats will make the first near-surface (<500 m altitude) measurements of solar wind flux and magnetic fields at swirls. NanoSWARM cubesats will also perform low-altitude neutron measurements to provide key constraints on the distribution of polar hydrogen concentrations, which are important volatile sinks in the lunar water cycle. To release its cubesats, NanoSWARM uses a high-heritage mother ship in a low altitude, polar, circular orbit. NanoSWARM's results will have direct applications to the geophysics, volatile distribution, and plasma physics of numerous other bodies, in particular asteroids and the terrestrial planets. The technologies and methods used by NanoSWARM will enable many new cubesat missions in the next decade, and expand the cubesat paradigm into deep space. NanoSWARM will be proposed as a NASA Discovery mission in early 2015.

  18. CCD-Based Imaging of Low-Energy Charged Particle Distribution Functions on ePOP and Swarm

    NASA Astrophysics Data System (ADS)

    Knudsen, D. J.; Burchill, J. K.

    2013-12-01

    The Canadian Enhanced Polar Outflow Probe (ePOP) and the European Space Agency's three Swarm satellites are being readied for launch in September and November 2013, respectively. Each will carry instruments that incorporate a novel CCD-based charged-particle detector to provide 64-pixel-diameter images of 2-D, low-energy charged particle distributions. The ePOP Suprathermal Electron Imager (SEI) will produce distribution functions in pitch angle and energy up to 200 eV at rates of up to 100 per second, with the goal of characterizing photo and suprathermal electrons that can drive ion outflow. The SEI can also image ion distributions up to 20 eV. ePOP will be launched on a Space-X Falcon 9 rocket into a polar elliptical orbit with an apogee of 1500 km. The Swarm satellites will be launched on a Russian Rokot vehicle into circular polar orbits, two at an initial altitude of 450 km, the third at 530 km. Swarm will measure magnetic and electric fields, the latter indirectly through ion drift detected by two Thermal Ion Imagers (TII) in each instrument, with the aid of Langmuir probe measurements of spacecraft potential and electron density and temperature. Electric fields measurements will be produced at a cadence of 2 per second to produce a picture of ionospheric electrodynamics at scales from 4 km to global. Due to special emphasis on measurement precision, Swarm will be able to resolve variations in Poynting flux as small as 1 microWatt per square meter. We gratefully acknowledge the ePOP SEI technical development team at the University of Calgary, and funding from the Canadian Space Agency. The Swarm Electric Field Instruments were built by a COM DEV Canada in collaboration with the University of Calgary and the Swedish Institute for Space Physics in Uppsala, with funding from ESA and CSA.

  19. A color image watermarking scheme based on hybrid classification method: Particle swarm optimization and k-nearest neighbor algorithm

    NASA Astrophysics Data System (ADS)

    Fındık, Oğuz; Babaoğlu, İsmail; Ülker, Erkan

    2010-12-01

    In this paper, a novel robust watermarking technique using particle swarm optimization and k-nearest neighbor algorithm is introduced to protect the intellectual property rights of color images in the spatial domain. In the embedding process, the color image is separated into non-overlapping blocks and each bit of the binary watermark is embedded into the individual blocks. Then, in order to extract the embedded watermark, features are obtained from watermark embedded blocks using the symmetric cross-shape kernel. These features are used to generate two centroids belonging to each binary (1 and 0) value of the watermark implementing particle swarm optimization. Subsequently, the embedded watermark is extracted by evaluating these centroids utilizing k-nearest neighbor algorithm. According to the test results, embedded watermark is extracted successfully even if the watermarked image is exposed to various image processing attacks.

  20. Short-term cascaded hydroelectric system scheduling based on chaotic particle swarm optimization using improved logistic map

    NASA Astrophysics Data System (ADS)

    He, Yaoyao; Yang, Shanlin; Xu, Qifa

    2013-07-01

    In order to solve the model of short-term cascaded hydroelectric system scheduling, a novel chaotic particle swarm optimization (CPSO) algorithm using improved logistic map is introduced, which uses the water discharge as the decision variables combined with the death penalty function. According to the principle of maximum power generation, the proposed approach makes use of the ergodicity, symmetry and stochastic property of improved logistic chaotic map for enhancing the performance of particle swarm optimization (PSO) algorithm. The new hybrid method has been examined and tested on two test functions and a practical cascaded hydroelectric system. The experimental results show that the effectiveness and robustness of the proposed CPSO algorithm in comparison with other traditional algorithms.

  1. Optimal management of saltwater intrusion in the coastal aquifer of Malia, Crete (Greece), using particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Karatzas, George P.; Dokou, Zoi

    2015-09-01

    Saltwater intrusion is a common phenomenon in coastal aquifers that can affect the quality of water intended for drinking and irrigation purposes. In order to provide sustainable management options for the coastal aquifer of Malia, located on the Greek island of Crete, a weighted multi-objective optimization methodology is employed. The methodology involves use of the particle swarm optimization algorithm combined with groundwater modelling. The sharp-interface approximation combined with the Ghyben-Herztberg equation is used to estimate the saltwater-intrusion front location. The prediction modelling results show that under the current pumping strategies (over-exploitation), the saltwater-intrusion front will continue to move inland, posing a serious threat to the groundwater quality. The management goal is to maximize groundwater withdrawal rates in the existing pumping wells while inhibiting the saltwater-intrusion front at locations closer to the coastal zone. This is achieved by requiring a minimum hydraulic-head value at pre-selected observation locations. In order to control the saltwater intrusion, a large number of pumping wells must be deactivated and alternative sources of water need to be considered.

  2. An implementation of particle swarm optimization to evaluate optimal under-voltage load shedding in competitive electricity markets

    NASA Astrophysics Data System (ADS)

    Hosseini-Bioki, M. M.; Rashidinejad, M.; Abdollahi, A.

    2013-11-01

    Load shedding is a crucial issue in power systems especially under restructured electricity environment. Market-driven load shedding in reregulated power systems associated with security as well as reliability is investigated in this paper. A technoeconomic multi-objective function is introduced to reveal an optimal load shedding scheme considering maximum social welfare. The proposed optimization problem includes maximum GENCOs and loads' profits as well as maximum loadability limit under normal and contingency conditions. Particle swarm optimization (PSO) as a heuristic optimization technique, is utilized to find an optimal load shedding scheme. In a market-driven structure, generators offer their bidding blocks while the dispatchable loads will bid their price-responsive demands. An independent system operator (ISO) derives a market clearing price (MCP) while rescheduling the amount of generating power in both pre-contingency and post-contingency conditions. The proposed methodology is developed on a 3-bus system and then is applied to a modified IEEE 30-bus test system. The obtained results show the effectiveness of the proposed methodology in implementing the optimal load shedding satisfying social welfare by maintaining voltage stability margin (VSM) through technoeconomic analyses.

  3. A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms.

    PubMed

    Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei

    2016-01-01

    Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object's mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field. PMID:26861348

  4. Forecasting of DST index from auroral electrojet indices using time-delay neural network + particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Lazzús, J. A.; López-Caraballo, C. H.; Rojas, P.; Salfate, I.; Rivera, M.; Palma-Chilla, L.

    2016-05-01

    In this study, an artificial neural network was optimized with particle swarm algorithm and trained to predict the geomagmetic DST index one hour ahead using the past values of DST and auroral electrojet indices. The results show that the proposed neural network model can be properly trained for predicting of DST(t + 1) with acceptable accuracy, and that the geomagnetic indices used have influential effects on the good training and predicting capabilities of the chosen network.

  5. A Random Time-Varying Particle Swarm Optimization for the Real Time Location Systems

    NASA Astrophysics Data System (ADS)

    Zhu, Hui; Tanabe, Yuji; Baba, Takaaki

    The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of applications. This paper presents a random time variable PSO algorithm, called the PSO-RTVIWAC, introducing random time-varying inertia weight and acceleration coefficients to significantly improve the performance of the original algorithms. The PSO-RTVIWAC method originates from the random inertia weight (PSO-RANDIW) and time-varying acceleration coefficients (PSO-TVAC) methods. Through the efficient control of search and convergence to the global optimum solution, the PSO-RTVIWAC method is capable of tracking and optimizing the position evaluate in the highly nonlinear real-time location systems (RTLS). Experimental results are compared with three previous PSO approaches from the literatures, showing that the new optimizer significantly outperforms previous approaches. Simply employing a few particles and iterations, a reasonable good positioning accuracy is obtained with the PSO-RTVIWAC method. This property makes the PSO-RTVIWAC method become more attractive since the computation efficiency is improved considerably, i.e. the computation can be completed in an extremely short time, which is crucial for the RTLS. By implementing a hardware design of PSO-RTVIWAC, the computations can simultaneously be performed using hardware to reduce the processing time. Due to a small number of particles and iterations, the hardware resource is saved and the area cost is reduced in the FPGA implementation. An improvement of positioning accuracy is observed with PSO-RTVIWAC method, compared with Taylor Series Expansion (TSE) and Genetic Algorithm (GA). Our experiments on the PSO-RTVIWAC to track and optimize the position evaluate have demonstrated that it is especially effective in dealing with optimization functions in the nonlinear dynamic environments.

  6. Calibration of Semi-analytic Models of Galaxy Formation Using Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Ruiz, Andrés N.; Cora, Sofía A.; Padilla, Nelson D.; Domínguez, Mariano J.; Vega-Martínez, Cristian A.; Tecce, Tomás E.; Orsi, Álvaro; Yaryura, Yamila; García Lambas, Diego; Gargiulo, Ignacio D.; Muñoz Arancibia, Alejandra M.

    2015-03-01

    We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observed galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.

  7. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

    PubMed Central

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634

  8. Automatic Calibration of a Semi-Distributed Hydrologic Model Using Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Bekele, E. G.; Nicklow, J. W.

    2005-12-01

    Hydrologic simulation models need to be calibrated and validated before using them for operational predictions. Spatially-distributed hydrologic models generally have a large number of parameters to capture the various physical characteristics of a hydrologic system. Manual calibration of such models is a very tedious and daunting task, and its success depends on the subjective assessment of a particular modeler, which includes knowledge of the basic approaches and interactions in the model. In order to alleviate these shortcomings, an automatic calibration model, which employs an evolutionary optimization technique known as Particle Swarm Optimizer (PSO) for parameter estimation, is developed. PSO is a heuristic search algorithm that is inspired by social behavior of bird flocking or fish schooling. The newly-developed calibration model is integrated to the U.S. Department of Agriculture's Soil and Water Assessment Tool (SWAT). SWAT is a physically-based, semi-distributed hydrologic model that was developed to predict the long term impacts of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use, and management conditions. SWAT was calibrated for streamflow and sediment concentration. The calibration process involves parameter specification, whereby sensitive model parameters are identified, and parameter estimation. In order to reduce the number of parameters to be calibrated, parameterization was performed. The methodology is applied to a demonstration watershed known as Big Creek, which is located in southern Illinois. Application results show the effectiveness of the approach and model predictions are significantly improved.

  9. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

    PubMed Central

    Subbulakshmi, C. V.; Deepa, S. N.

    2015-01-01

    Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers. PMID:26491713

  10. a Hybrid Approach of Neural Network with Particle Swarm Optimization for Tobacco Pests Prediction

    NASA Astrophysics Data System (ADS)

    Lv, Jiake; Wang, Xuan; Xie, Deti; Wei, Chaofu

    Forecasting pests emergence levels plays a significant role in regional crop planting and management. The accuracy, which is derived from the accuracy of the forecasting approach used, will determine the economics of the operation of the pests prediction. Conventional methods including time series, regression analysis or ARMA model entail exogenous input together with a number of assumptions. The use of neural networks has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with some drawbacks such as very slow convergence and easy entrapment in a local minimum. This paper presents a hybrid approach of neural network with particle swarm optimization for developing the accuracy of predictions. The approach is applied to forecast Alternaria alternate Keissl emergence level of the WuLong Country, one of the most important tobacco planting areas in Chongqing. Traditional ARMA model and BP neural network are investigated as comparison basis. The experimental results show that the proposed approach can achieve better prediction performance.

  11. Evacuation dynamic and exit optimization of a supermarket based on particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Li, Lin; Yu, Zhonghai; Chen, Yang

    2014-12-01

    A modified particle swarm optimization algorithm is proposed in this paper to investigate the dynamic of pedestrian evacuation from a fire in a public building-a supermarket with multiple exits and configurations of counters. Two distinctive evacuation behaviours featured by the shortest-path strategy and the following-up strategy are simulated in the model, accounting for different categories of age and sex of the pedestrians along with the impact of the fire, including gases, heat and smoke. To examine the relationship among the progress of the overall evacuation and the layout and configuration of the site, a series of simulations are conducted in various settings: without a fire and with a fire at different locations. Those experiments reveal a general pattern of two-phase evacuation, i.e., a steep section and a flat section, in addition to the impact of the presence of multiple exits on the evacuation along with the geographic locations of the exits. For the study site, our simulations indicated the deficiency of the configuration and the current layout of this site in the process of evacuation and verified the availability of proposed solutions to resolve the deficiency. More specifically, for improvement of the effectiveness of the evacuation from the site, adding an exit between Exit 6 and Exit 7 and expanding the corridor at the right side of Exit 7 would significantly reduce the evacuation time.

  12. Particle Swarm Optimization Applied to EEG Source Localization of Somatosensory Evoked Potentials.

    PubMed

    Shirvany, Yazdan; Mahmood, Qaiser; Edelvik, Fredrik; Jakobsson, Stefan; Hedstrom, Anders; Persson, Mikael

    2014-01-01

    One of the most important steps in presurgical diagnosis of medically intractable epilepsy is to find the precise location of the epileptogenic foci. Electroencephalography (EEG) is a noninvasive tool commonly used at epilepsy surgery centers for presurgical diagnosis. In this paper, a modified particle swarm optimization (MPSO) method is used to solve the EEG source localization problem. The method is applied to noninvasive EEG recording of somatosensory evoked potentials (SEPs) for a healthy subject. A 1 mm hexahedra finite element volume conductor model of the subject's head was generated using T1-weighted magnetic resonance imaging data. Special consideration was made to accurately model the skull and cerebrospinal fluid. An exhaustive search pattern and the MPSO method were then applied to the peak of the averaged SEP data and both identified the same region of the somatosensory cortex as the location of the SEP source. A clinical expert independently identified the expected source location, further corroborating the source analysis methods. The MPSO converged to the global minima with significantly lower computational complexity compared to the exhaustive search method that required almost 3700 times more evaluations. PMID:24122569

  13. Application of particle swarm optimization method to incoherent scatter radar measurement of ionosphere parameters

    NASA Astrophysics Data System (ADS)

    Wu, Li-Li; Zhou, Qihou H.; Chen, Tie-Jun; Liang, J. J.; Wu, Xin

    2015-09-01

    Simultaneous derivation of multiple ionospheric parameters from the incoherent scatter power spectra in the F1 region is difficult because the spectra have only subtle differences for different combinations of parameters. In this study, we apply a particle swarm optimizer (PSO) to incoherent scatter power spectrum fitting and compare it to the commonly used least squares fitting (LSF) technique. The PSO method is found to outperform the LSF method in practically all scenarios using simulated data. The PSO method offers the advantages of not being sensitive to initial assumptions and allowing physical constraints to be easily built into the model. When simultaneously fitting for molecular ion fraction (fm), ion temperature (Ti), and ratio of ion to electron temperature (γT), γT is largely stable. The uncertainty between fm and Ti can be described as a quadratic relationship. The significance of this result is that Ti can be retroactively corrected for data archived many years ago where the assumption of fm may not be accurate, and the original power spectra are unavailable. In our discussion, we emphasize the fitting for fm, which is a difficult parameter to obtain. PSO method is often successful in obtaining fm, whereas LSF fails. We apply both PSO and LSF to actual observations made by the Arecibo incoherent scatter radar. The results show that PSO method is a viable method to simultaneously determine ion and electron temperatures and molecular ion fraction when the last is greater than 0.3.

  14. Enhancing speech recognition using improved particle swarm optimization based hidden Markov model.

    PubMed

    Selvaraj, Lokesh; Ganesan, Balakrishnan

    2014-01-01

    Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy. PMID:25478588

  15. Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm

    PubMed Central

    Naebi, Mohammad; Saberi, Eshaghali; Risbaf Fakour, Sirous; Naebi, Ahmad; Hosseini Tabatabaei, Somayeh; Ansari Moghadam, Somayeh; Bozorgmehr, Elham; Davtalab Behnam, Nasim; Azimi, Hamidreza

    2016-01-01

    Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO) and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%. PMID:27212947

  16. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier.

    PubMed

    Subbulakshmi, C V; Deepa, S N

    2015-01-01

    Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers. PMID:26491713

  17. Designing a mirrored Howland circuit with a particle swarm optimisation algorithm

    NASA Astrophysics Data System (ADS)

    Bertemes-Filho, Pedro; Negri, Lucas H.; Vincence, Volney C.

    2016-06-01

    Electrical impedance spectroscopy usually requires a wide bandwidth current source with high output impedance. Non-idealities of the operational amplifier (op-amp) degrade its performance. This work presents a particle swarm algorithm for extracting the main AC characteristics of the op-amp used to design a mirrored modified Howland current source circuit which satisfies both the output current and the impedance spectra required. User specifications were accommodated. Both resistive and biological loads were used in the simulations. The results showed that the algorithm can correctly identify the open-loop gain and the input and output resistance of the op-amp which best fit the performance requirements of the circuit. It was also shown that the higher the open-loop gain corner frequency the higher the output impedance of the circuit. The algorithm could be a powerful tool for developing a desirable current source for different bioimpedance medical and clinical applications, such as cancer tissue characterisation and tissue cell measurements.

  18. Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization

    PubMed Central

    Niu, Liyong; Zhang, Di

    2015-01-01

    Electric taxis are playing an important role in the application of electric vehicles. The actual operational data of electric taxis in Shenzhen, China, is analyzed, and, in allusion to the unbalanced time availability of the charging station equipment, the electric taxis charging guidance system is proposed basing on the charging station information and vehicle information. An electric taxis charging guidance model is established and guides the charging based on the positions of taxis and charging stations with adaptive mutation particle swarm optimization. The simulation is based on the actual data of Shenzhen charging stations, and the results show that electric taxis can be evenly distributed to the appropriate charging stations according to the charging pile numbers in charging stations after the charging guidance. The even distribution among the charging stations in the area will be achieved and the utilization of charging equipment will be improved, so the proposed charging guidance method is verified to be feasible. The improved utilization of charging equipment can save public charging infrastructure resources greatly. PMID:26236770

  19. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    PubMed

    Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634

  20. Control of conducting polymer actuators without physical feedback: simulated feedback control approach with particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Xiang, Xingcan; Mutlu, Rahim; Alici, Gursel; Li, Weihua

    2014-03-01

    Conducting polymer actuators have shown significant potential in articulating micro instruments, manipulation devices, and robotics. However, implementing a feedback control strategy to enhance their positioning ability and accuracy in any application requires a feedback sensor, which is extremely large in size compared to the size of the actuators. Therefore, this paper proposes a new sensorless control scheme without the use of a position feedback sensor. With the help of the system identification technique and particle swarm optimization, the control scheme, which we call the simulated feedback control system, showed a satisfactory command tracking performance for the conducting polymer actuator’s step and dynamic displacement responses, especially under a disturbance, without needing a physical feedback loop, but using a simulated feedback loop. The primary contribution of this study is to propose and experimentally evaluate the simulated feedback control scheme for a class of the conducting polymer actuators known as tri-layer polymer actuators, which can operate both in dry and wet media. This control approach can also be extended to other smart actuators or systems, for which the feedback control based on external sensing is impractical.

  1. Motion generation of peristaltic mobile robot with particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Homma, Takahiro; Kamamichi, Norihiro

    2015-03-01

    In developments of robots, bio-mimetics is attracting attention, which is a technology for the design of the structure and function inspired from biological system. There are a lot of examples of bio-mimetics in robotics such as legged robots, flapping robots, insect-type robots, fish-type robots. In this study, we focus on the motion of earthworm and aim to develop a peristaltic mobile robot. The earthworm is a slender animal moving in soil. It has a segmented body, and each segment can be shorted and lengthened by muscular actions. It can move forward by traveling expanding motions of each segment backward. By mimicking the structure and motion of the earthworm, we can construct a robot with high locomotive performance against an irregular ground or a narrow space. In this paper, to investigate the motion analytically, a dynamical model is introduced, which consist of a series-connected multi-mass model. Simple periodic patterns which mimic the motions of earthworms are applied in an open-loop fashion, and the moving patterns are verified through numerical simulations. Furthermore, to generate efficient motion of the robot, a particle swarm optimization algorithm, one of the meta-heuristic optimization, is applied. The optimized results are investigated by comparing to simple periodic patterns.

  2. Tabu search and binary particle swarm optimization for feature selection using microarray data.

    PubMed

    Chuang, Li-Yeh; Yang, Cheng-Huei; Yang, Cheng-Hong

    2009-12-01

    Gene expression profiles have great potential as a medical diagnosis tool because they represent the state of a cell at the molecular level. In the classification of cancer type research, available training datasets generally have a fairly small sample size compared to the number of genes involved. This fact poses an unprecedented challenge to some classification methodologies due to training data limitations. Therefore, a good selection method for genes relevant for sample classification is needed to improve the predictive accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this article, we propose to combine tabu search (TS) and binary particle swarm optimization (BPSO) for feature selection. BPSO acts as a local optimizer each time the TS has been run for a single generation. The K-nearest neighbor method with leave-one-out cross-validation and support vector machine with one-versus-rest serve as evaluators of the TS and BPSO. The proposed method is applied and compared to the 11 classification problems taken from the literature. Experimental results show that our method simplifies features effectively and either obtains higher classification accuracy or uses fewer features compared to other feature selection methods. PMID:20047491

  3. Automatic Parameter Tuning for the Morpheus Vehicle Using Particle Swarm Optimization

    NASA Technical Reports Server (NTRS)

    Birge, B.

    2013-01-01

    A high fidelity simulation using a PC based Trick framework has been developed for Johnson Space Center's Morpheus test bed flight vehicle. There is an iterative development loop of refining and testing the hardware, refining the software, comparing the software simulation to hardware performance and adjusting either or both the hardware and the simulation to extract the best performance from the hardware as well as the most realistic representation of the hardware from the software. A Particle Swarm Optimization (PSO) based technique has been developed that increases speed and accuracy of the iterative development cycle. Parameters in software can be automatically tuned to make the simulation match real world subsystem data from test flights. Special considerations for scale, linearity, discontinuities, can be all but ignored with this technique, allowing fast turnaround both for simulation tune up to match hardware changes as well as during the test and validation phase to help identify hardware issues. Software models with insufficient control authority to match hardware test data can be immediately identified and using this technique requires very little to no specialized knowledge of optimization, freeing model developers to concentrate on spacecraft engineering. Integration of the PSO into the Morpheus development cycle will be discussed as well as a case study highlighting the tool's effectiveness.

  4. Chaos particle swarm optimization combined with circular median filtering for geophysical parameters retrieval from Windsat

    NASA Astrophysics Data System (ADS)

    Zhang, Lei; Wang, Zhenzhan; Shi, Hanqing; Long, Zhiyong; Du, Huadong

    2016-08-01

    This paper established a geophysical retrieval algorithm for sea surface wind vector, sea surface temperature, columnar atmospheric water vapor, and columnar cloud liquid water from WindSat, using the measured brightness temperatures and a matchup database. To retrieve the wind vector, a chaotic particle swarm approach was used to determine a set of possible wind vector solutions which minimize the difference between the forward model and the WindSat observations. An adjusted circular median filtering function was adopted to remove wind direction ambiguity. The validation of the wind speed, wind direction, sea surface temperature, columnar atmospheric water vapor, and columnar liquid cloud water indicates that this algorithm is feasible and reasonable and can be used to retrieve these atmospheric and oceanic parameters. Compared with moored buoy data, the RMS errors for wind speed and sea surface temperature were 0.92 m s-1 and 0.88°C, respectively. The RMS errors for columnar atmospheric water vapor and columnar liquid cloud water were 0.62 mm and 0.01 mm, respectively, compared with F17 SSMIS results. In addition, monthly average results indicated that these parameters are in good agreement with AMSR-E results. Wind direction retrieval was studied under various wind speed conditions and validated by comparing to the QuikSCAT measurements, and the RMS error was 13.3°. This paper offers a new approach to the study of ocean wind vector retrieval using a polarimetric microwave radiometer.

  5. A novel adaptive compression method for hyperspectral images by using EDT and particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Ghamisi, Pedram; Kumar, Lalit

    2012-01-01

    Hyperspectral sensors generate useful information about climate and the earth surface in numerous contiguous narrow spectral bands, and are widely used in resource management, agriculture, environmental monitoring, etc. Compression of the hyperspectral data helps in long-term storage and transmission systems. Lossless compression is preferred for high-detail data, such as hyperspectral data. Due to high redundancy in neighboring spectral bands and the tendency to achieve a higher compression ratio, using adaptive coding methods for hyperspectral data seems suitable for this purpose. This paper introduces two new compression methods. One of these methods is adaptive and powerful for the compression of hyperspectral data, which is based on separating the bands with different specifications by the histogram and Binary Particle Swarm Optimization (BPSO) and compressing each one a different manner. The new proposed methods improve the compression ratio of the JPEG standards and save storage space the transmission. The proposed methods are applied on different test cases, and the results are evaluated and compared with some other compression methods, such as lossless JPEG and JPEG2000.

  6. Design of fiber coupled Er:chalcogenide microsphere amplifier via particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Palma, Giuseppe; Bia, Pietro; Mescia, Luciano; Yano, Tetsuji; Nazabal, Virginie; Taguchi, Jun; Moréac, Alain; Prudenzano, Francesco

    2014-07-01

    A mid-IR amplifier consisting of a tapered chalcogenide fiber coupled to an Er-doped chalcogenide microsphere has been optimized via a particle swarm optimization (PSO) approach. More precisely, a dedicated three-dimensional numerical model, based on the coupled mode theory and solving the rate equations, has been integrated with the PSO procedure. The rate equations have included the main transitions among the erbium energy levels, the amplified spontaneous emission, and the most important secondary transitions pertaining to the ion-ion interactions. The PSO has allowed the optimal choice of the microsphere and fiber radius, taper angle, and fiber-microsphere gap in order to maximize the amplifier gain. The taper angle and the fiber-microsphere gap have been optimized to efficiently inject into the microsphere both the pump and the signal beams and to improve their spatial overlapping with the rare-earth-doped region. The employment of the PSO approach shows different attractive features, especially when many parameters have to be optimized. The numerical results demonstrate the effectiveness of the proposed approach for the design of amplifying systems. The PSO-based optimization approach has allowed the design of a microsphere-based amplifying system more efficient than a similar device designed by using a deterministic optimization method. In fact, the amplifier designed via the PSO exhibits a simulated gain G=33.7 dB, which is higher than the gain G=6.9 dB of the amplifier designed via the deterministic method.

  7. Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control

    PubMed Central

    Qazi, Abroon Jamal; de Silva, Clarence W.

    2014-01-01

    This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control. PMID:24574868

  8. Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm

    PubMed Central

    2014-01-01

    Background In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. Results To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Conclusion Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. PMID:24555567

  9. Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization.

    PubMed

    Niu, Liyong; Zhang, Di

    2015-01-01

    Electric taxis are playing an important role in the application of electric vehicles. The actual operational data of electric taxis in Shenzhen, China, is analyzed, and, in allusion to the unbalanced time availability of the charging station equipment, the electric taxis charging guidance system is proposed basing on the charging station information and vehicle information. An electric taxis charging guidance model is established and guides the charging based on the positions of taxis and charging stations with adaptive mutation particle swarm optimization. The simulation is based on the actual data of Shenzhen charging stations, and the results show that electric taxis can be evenly distributed to the appropriate charging stations according to the charging pile numbers in charging stations after the charging guidance. The even distribution among the charging stations in the area will be achieved and the utilization of charging equipment will be improved, so the proposed charging guidance method is verified to be feasible. The improved utilization of charging equipment can save public charging infrastructure resources greatly. PMID:26236770

  10. Optimization of Electric Power Leveling Systems by using Taper-Off-Reflectance Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Makino, Yohei; Fujii, Toshinori; Imai, Jun; Funabiki, Shigeyuki

    Recently, it is desired to develop energy control technologies for environmental issues such as global warming and exhaustion of fossil fuel. Power fluctuations in large power consumers may cause the instability of electric power systems and increase the cost of the electric power facility and electricity charges. Developing the electric power-leveling systems (EPLS) to compensate the power fluctuations is necessary for future electric power systems. Now, EPLS with an SMES have been proposed as one of the countermeasures for the electric power quality improvement. The SMES is superior to other energy storage devices in response and storage efficiency. The authors proposed the EPLS based on fussy control with the SMES. For this practical implementation, optimizing control gain and SMES capacity is an important issue. This paper proposes a new optimization method of the EPLS. The proposed algorithm is novel particle swarm optimization based on taper-off reflectance (TRPSO). The proposed TRPSO optimizes the design variables of the EPLS efficiently and effectively.

  11. Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization

    PubMed Central

    Lin, Yuqun

    2014-01-01

    The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system. PMID:24683334

  12. Gravity inversion and uncertainty assessment of basement relief via Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Pallero, J. L. G.; Fernández-Martínez, J. L.; Bonvalot, S.; Fudym, O.

    2015-05-01

    Gravity inversion is a classical tool in applied geophysics that corresponds, both, to a linear (density unknown) or nonlinear (geometry unknown) inverse problem depending on the model parameters. Inversion of basement relief of sedimentary basins is an important application among the nonlinear techniques. A common way to approach this problem consists in discretizing the basin using polygons (or other geometries), and iteratively solving the nonlinear inverse problem by local optimization. Nevertheless, this kind of approach is highly dependent of the prior information that is used and lacks from a correct solution appraisal (nonlinear uncertainty analysis). In this paper, we present the application of a full family Particle Swarm Optimizers (PSO) to the 2D gravity inversion and model appraisal (uncertainty assessment) of basement relief in sedimentary basins. The application of these algorithms to synthetic and real cases (a gravimetric profile from Atacama Desert in north Chile) shows that it is possible to perform a fast inversion and uncertainty assessment of the gravimetric model using a sampling while optimizing procedure. Besides, the parameters of these exploratory PSO optimizers are automatically tuned and selected based on stability criteria. We also show that the result is robust to the presence of noise in data. The fact that these algorithms do not require large computational resources makes them very attractive to solve this kind of gravity inversion problems.

  13. Particle swarm optimization-based continuous cellular automaton for the simulation of deep reactive ion etching

    NASA Astrophysics Data System (ADS)

    Li, Yuan; Gosálvez, Miguel A.; Pal, Prem; Sato, Kazuo; Xing, Yan

    2015-05-01

    We combine the particle swarm optimization (PSO) method and the continuous cellular automaton (CCA) in order to simulate deep reactive ion etching (DRIE), also known as the Bosch process. By considering a generic growth/etch process, the proposed PSO-CCA method provides a general, integrated procedure to optimize the parameter values of any given theoretical model conceived to describe the corresponding experiments, which are simulated by the CCA method. To stress the flexibility of the PSO-CCA method, two different theoretical models of the DRIE process are used, namely, the ballistic transport and reaction (BTR) model, and the reactant concentration (RC) model. DRIE experiments are designed and conducted to compare the simulation results with the experiments on different machines and process conditions. Previously reported experimental data are also considered to further test the flexibility of the proposed method. The agreement between the simulations and experiments strongly indicates that the PSO-CCA method can be used to adjust the theoretical parameters by using a limited amount of experimental data. The proposed method has the potential to be applied on the modeling and optimization of other growth/etch processes.

  14. Power and Time Slot Allocation in Cognitive Relay Networks Using Particle Swarm Optimization

    PubMed Central

    Dadashzadeh, Gholamreza; Razzazi, Farbod; Razavizadeh, S. Mohammad

    2013-01-01

    The two main problems in cognitive radio networks are power and time slot allocation problems which require a precise analysis and guarantee the quality of service in both the primary and secondary users. In this paper, these two problems are considered and a method is proposed to solve the resulting optimization problem. Our proposed method provides an improved performance in solving the constrained nonlinear multiobject optimization for the power control and beamforming in order to reach the maximum capacity and proper adaption of time slots, and as a result a new scheme for joint power and time slot allocation in cognitive relay networks is proposed. We adopt space diversity access as the secondary users access scheme and divide the time between multiple secondary users according to their contribution to primary user's transmission. Helping primary users provides more opportunities for secondary users to access the channel since the primary users can release the channel sooner. In contrast, primary network leases portion of channel access time to the secondary users for their transmission using particle swarm optimization (PSO). Numerical studies show good performance of the proposed scheme with a dynamic cost function in a nonstationary environment. PMID:24228002

  15. An Accelerated Particle Swarm Optimization Algorithm on Parametric Optimization of WEDM of Die-Steel

    NASA Astrophysics Data System (ADS)

    Muthukumar, V.; Suresh Babu, A.; Venkatasamy, R.; Senthil Kumar, N.

    2015-01-01

    This study employed Accelerated Particle Swarm Optimization (APSO) algorithm to optimize the machining parameters that lead to a maximum Material Removal Rate (MRR), minimum surface roughness and minimum kerf width values for Wire Electrical Discharge Machining (WEDM) of AISI D3 die-steel. Four machining parameters that are optimized using APSO algorithm include Pulse on-time, Pulse off-time, Gap voltage, Wire feed. The machining parameters are evaluated by Taguchi's L9 Orthogonal Array (OA). Experiments are conducted on a CNC WEDM and output responses such as material removal rate, surface roughness and kerf width are determined. The empirical relationship between control factors and output responses are established by using linear regression models using Minitab software. Finally, APSO algorithm, a nature inspired metaheuristic technique, is used to optimize the WEDM machining parameters for higher material removal rate and lower kerf width with surface roughness as constraint. The confirmation experiments carried out with the optimum conditions show that the proposed algorithm was found to be potential in finding numerous optimal input machining parameters which can fulfill wide requirements of a process engineer working in WEDM industry.

  16. Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm.

    PubMed

    Naebi, Mohammad; Saberi, Eshaghali; Risbaf Fakour, Sirous; Naebi, Ahmad; Hosseini Tabatabaei, Somayeh; Ansari Moghadam, Somayeh; Bozorgmehr, Elham; Davtalab Behnam, Nasim; Azimi, Hamidreza

    2016-01-01

    Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO) and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%. PMID:27212947

  17. Performance analysis of a semiactive suspension system with particle swarm optimization and fuzzy logic control.

    PubMed

    Qazi, Abroon Jamal; de Silva, Clarence W; Khan, Afzal; Khan, Muhammad Tahir

    2014-01-01

    This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control. PMID:24574868

  18. Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method

    NASA Astrophysics Data System (ADS)

    Rahman, Md Ashiqur; Anwar, Sohel; Izadian, Afshin

    2016-03-01

    In this paper, a gradient-free optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized to identify specific parameters of the electrochemical model of a Lithium-Ion battery with LiCoO2 cathode chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, over-discharged battery, over-charged battery, etc. It is important for a battery management system to have these parameter changes fully captured in a bank of battery models that can be used to monitor battery conditions in real time. Here the PSO methodology has been successfully applied to identify four electrochemical model parameters that exhibit significant variations under severe operating conditions: solid phase diffusion coefficient at the positive electrode (cathode), solid phase diffusion coefficient at the negative electrode (anode), intercalation/de-intercalation reaction rate at the cathode, and intercalation/de-intercalation reaction rate at the anode. The identified model parameters were used to generate the respective battery models for both healthy and degraded batteries. These models were then validated by comparing the model output voltage with the experimental output voltage for the stated operating conditions. The identified Li-Ion battery electrochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results.

  19. Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model

    PubMed Central

    Selvaraj, Lokesh; Ganesan, Balakrishnan

    2014-01-01

    Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy. PMID:25478588

  20. Tunning PID controller using particle swarm optimization algorithm on automatic voltage regulator system

    NASA Astrophysics Data System (ADS)

    Aranza, M. F.; Kustija, J.; Trisno, B.; Hakim, D. L.

    2016-04-01

    PID Controller (Proportional Integral Derivative) was invented since 1910, but till today still is used in industries, even though there are many kind of modern controllers like fuzz controller and neural network controller are being developed. Performance of PID controller is depend on on Proportional Gain (Kp), Integral Gain (Ki) and Derivative Gain (Kd). These gains can be got by using method Ziegler-Nichols (ZN), gain-phase margin, Root Locus, Minimum Variance dan Gain Scheduling however these methods are not optimal to control systems that nonlinear and have high-orde, in addition, some methods relative hard. To solve those obstacles, particle swarm optimization (PSO) algorithm is proposed to get optimal Kp, Ki and Kd. PSO is proposed because PSO has convergent result and not require many iterations. On this research, PID controller is applied on AVR (Automatic Voltage Regulator). Based on result of analyzing transient, stability Root Locus and frequency response, performance of PID controller is better than Ziegler-Nichols.

  1. Particle Swarm Optimization Algorithm for Optimizing Assignment of Blood in Blood Banking System

    PubMed Central

    Olusanya, Micheal O.; Arasomwan, Martins A.; Adewumi, Aderemi O.

    2015-01-01

    This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients' blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment problem (BAP) introduced recently in literature. We propose a queue and multiple knapsack models with PSO-based solution to address this challenge. Simulation is based on sets of randomly generated data that mimic real-world population distribution of blood types. Results obtained show the efficiency of the proposed algorithm for BAP with no blood units wasted and very low importation, where necessary, from outside the blood bank. The result therefore can serve as a benchmark and basis for decision support tools for real-life deployment. PMID:25815046

  2. Particle swarm optimization algorithm for optimizing assignment of blood in blood banking system.

    PubMed

    Olusanya, Micheal O; Arasomwan, Martins A; Adewumi, Aderemi O

    2015-01-01

    This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients' blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment problem (BAP) introduced recently in literature. We propose a queue and multiple knapsack models with PSO-based solution to address this challenge. Simulation is based on sets of randomly generated data that mimic real-world population distribution of blood types. Results obtained show the efficiency of the proposed algorithm for BAP with no blood units wasted and very low importation, where necessary, from outside the blood bank. The result therefore can serve as a benchmark and basis for decision support tools for real-life deployment. PMID:25815046

  3. Segmentation of deformable organs from medical images using particle swarm optimization and nonlinear shape priors

    NASA Astrophysics Data System (ADS)

    Afifi, Ahmed; Nakaguchi, Toshiya; Tsumura, Norimichi

    2010-03-01

    In many medical applications, the automatic segmentation of deformable organs from medical images is indispensable and its accuracy is of a special interest. However, the automatic segmentation of these organs is a challenging task according to its complex shape. Moreover, the medical images usually have noise, clutter, or occlusion and considering the image information only often leads to meager image segmentation. In this paper, we propose a fully automated technique for the segmentation of deformable organs from medical images. In this technique, the segmentation is performed by fitting a nonlinear shape model with pre-segmented images. The kernel principle component analysis (KPCA) is utilized to capture the complex organs deformation and to construct the nonlinear shape model. The presegmentation is carried out by labeling each pixel according to its high level texture features extracted using the overcomplete wavelet packet decomposition. Furthermore, to guarantee an accurate fitting between the nonlinear model and the pre-segmented images, the particle swarm optimization (PSO) algorithm is employed to adapt the model parameters for the novel images. In this paper, we demonstrate the competence of proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans of different patients.

  4. An MR brain images classifier system via particle swarm optimization and kernel support vector machine.

    PubMed

    Zhang, Yudong; Wang, Shuihua; Ji, Genlin; Dong, Zhengchao

    2013-01-01

    Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA) to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM) with RBF kernel, using particle swarm optimization (PSO) to optimize the parameters C and σ . Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick's disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM. PMID:24163610

  5. A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

    SciTech Connect

    Li, Jinglai; Lin, Guang; Yang, Xu

    2015-09-01

    In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by three steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.

  6. Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval

    PubMed Central

    Hashim, Rathiah; Noor Elaiza, Abd Khalid; Irtaza, Aun

    2014-01-01

    One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations. PMID:25121136

  7. Particle Swarm Optimization of Low-Thrust, Geocentric-to-Halo-Orbit Transfers

    NASA Astrophysics Data System (ADS)

    Abraham, Andrew J.

    Missions to Lagrange points are becoming increasingly popular amongst spacecraft mission planners. Lagrange points are locations in space where the gravity force from two bodies, and the centrifugal force acting on a third body, cancel. To date, all spacecraft that have visited a Lagrange point have done so using high-thrust, chemical propulsion. Due to the increasing availability of low-thrust (high efficiency) propulsive devices, and their increasing capability in terms of fuel efficiency and instantaneous thrust, it has now become possible for a spacecraft to reach a Lagrange point orbit without the aid of chemical propellant. While at any given time there are many paths for a low-thrust trajectory to take, only one is optimal. The traditional approach to spacecraft trajectory optimization utilizes some form of gradient-based algorithm. While these algorithms offer numerous advantages, they also have a few significant shortcomings. The three most significant shortcomings are: (1) the fact that an initial guess solution is required to initialize the algorithm, (2) the radius of convergence can be quite small and can allow the algorithm to become trapped in local minima, and (3) gradient information is not always assessable nor always trustworthy for a given problem. To avoid these problems, this dissertation is focused on optimizing a low-thrust transfer trajectory from a geocentric orbit to an Earth-Moon, L1, Lagrange point orbit using the method of Particle Swarm Optimization (PSO). The PSO method is an evolutionary heuristic that was originally written to model birds swarming to locate hidden food sources. This PSO method will enable the exploration of the invariant stable manifold of the target Lagrange point orbit in an effort to optimize the spacecraft's low-thrust trajectory. Examples of these optimized trajectories are presented and contrasted with those found using traditional, gradient-based approaches. In summary, the results of this dissertation find

  8. Hyperspectral band selection based on parallel particle swarm optimization and impurity function band prioritization schemes

    NASA Astrophysics Data System (ADS)

    Chang, Yang-Lang; Liu, Jin-Nan; Chen, Yen-Lin; Chang, Wen-Yen; Hsieh, Tung-Ju; Huang, Bormin

    2014-01-01

    In recent years, satellite imaging technologies have resulted in an increased number of bands acquired by hyperspectral sensors, greatly advancing the field of remote sensing. Accordingly, owing to the increasing number of bands, band selection in hyperspectral imagery for dimension reduction is important. This paper presents a framework for band selection in hyperspectral imagery that uses two techniques, referred to as particle swarm optimization (PSO) band selection and the impurity function band prioritization (IFBP) method. With the PSO band selection algorithm, highly correlated bands of hyperspectral imagery can first be grouped into modules to coarsely reduce high-dimensional datasets. Then, these highly correlated band modules are analyzed with the IFBP method to finely select the most important feature bands from the hyperspectral imagery dataset. However, PSO band selection is a time-consuming procedure when the number of hyperspectral bands is very large. Hence, this paper proposes a parallel computing version of PSO, namely parallel PSO (PPSO), using a modern graphics processing unit (GPU) architecture with NVIDIA's compute unified device architecture technology to improve the computational speed of PSO processes. The natural parallelism of the proposed PPSO lies in the fact that each particle can be regarded as an independent agent. Parallel computation benefits the algorithm by providing each agent with a parallel processor. The intrinsic parallel characteristics embedded in PPSO are, therefore, suitable for parallel computation. The effectiveness of the proposed PPSO is evaluated through the use of airborne visible/infrared imaging spectrometer hyperspectral images. The performance of PPSO is validated using the supervised K-nearest neighbor classifier. The experimental results demonstrate that the proposed PPSO/IFBP band selection method can not only improve computational speed, but also offer a satisfactory classification performance.

  9. Dynamic multi-swarm particle swarm optimizer using parallel PC cluster systems for global optimization of large-scale multimodal functions

    NASA Astrophysics Data System (ADS)

    Fan, Shu-Kai S.; Chang, Ju-Ming

    2010-05-01

    This article presents a novel parallel multi-swarm optimization (PMSO) algorithm with the aim of enhancing the search ability of standard single-swarm PSOs for global optimization of very large-scale multimodal functions. Different from the existing multi-swarm structures, the multiple swarms work in parallel, and the search space is partitioned evenly and dynamically assigned in a weighted manner via the roulette wheel selection (RWS) mechanism. This parallel, distributed framework of the PMSO algorithm is developed based on a master-slave paradigm, which is implemented on a cluster of PCs using message passing interface (MPI) for information interchange among swarms. The PMSO algorithm handles multiple swarms simultaneously and each swarm performs PSO operations of its own independently. In particular, one swarm is designated for global search and the others are for local search. The first part of the experimental comparison is made among the PMSO, standard PSO, and two state-of-the-art algorithms (CTSS and CLPSO) in terms of various un-rotated and rotated benchmark functions taken from the literature. In the second part, the proposed multi-swarm algorithm is tested on large-scale multimodal benchmark functions up to 300 dimensions. The results of the PMSO algorithm show great promise in solving high-dimensional problems.

  10. Slow light performance enhancement of Bragg slot photonic crystal waveguide with particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Abedi, Kambiz; Mirjalili, Seyed Mohammad

    2015-03-01

    Recently, majority of current research in the field of designing Phonic Crystal Waveguides (PCW) focus in extracting the relations between output slow light properties of PCW and structural parameters through a huge number of tedious non-systematic simulations in order to introduce better designs. This paper proposes a novel systematic approach which can be considered as a shortcut to alleviate the difficulties and human involvements in designing PCWs. In the proposed method, the problem of PCW design is first formulated as an optimization problem. Then, an optimizer is employed in order to automatically find the optimum design for the formulated PCWs. Meanwhile, different constraints are also considered during optimization with the purpose of applying physical limitations to the final optimum structure. As a case study, the structure of a Bragg-like Corrugation Slotted PCWs (BCSPCW) is optimized by using the proposed method. One of the most computationally powerful techniques in Computational Intelligence (CI) called Particle Swarm Optimization (PSO) is employed as an optimizer to automatically find the optimum structure for BCSPCW. The optimization process is done by considering five constraints to guarantee the feasibility of the final optimized structures and avoid band mixing. Numerical results demonstrate that the proposed method is able to find an optimum structure for BCSPCW with 172% and 100% substantial improvements in the bandwidth and Normalized Delay-Bandwidth Product (NDBP) respectively compared to the best current structure in the literature. Moreover, there is a time domain analysis at the end of the paper which verifies the performance of the optimized structure and proves that this structure has low distortion and attenuation simultaneously.

  11. A Particle Swarm Optimization Algorithm for Optimal Operating Parameters of VMI Systems in a Two-Echelon Supply Chain

    NASA Astrophysics Data System (ADS)

    Sue-Ann, Goh; Ponnambalam, S. G.

    This paper focuses on the operational issues of a Two-echelon Single-Vendor-Multiple-Buyers Supply chain (TSVMBSC) under vendor managed inventory (VMI) mode of operation. To determine the optimal sales quantity for each buyer in TSVMBC, a mathematical model is formulated. Based on the optimal sales quantity can be obtained and the optimal sales price that will determine the optimal channel profit and contract price between the vendor and buyer. All this parameters depends upon the understanding of the revenue sharing between the vendor and buyers. A Particle Swarm Optimization (PSO) is proposed for this problem. Solutions obtained from PSO is compared with the best known results reported in literature.

  12. A Novel Hybrid Statistical Particle Swarm Optimization for Multimodal Functions and Frequency Control of Hybrid Wind-Solar System

    NASA Astrophysics Data System (ADS)

    Verma, Harish Kumar; Jain, Cheshta

    2015-07-01

    In this article, a hybrid algorithm of particle swarm optimization (PSO) with statistical parameter (HSPSO) is proposed. Basic PSO for shifted multimodal problems have low searching precision due to falling into a number of local minima. The proposed approach uses statistical characteristics to update the velocity of the particle to avoid local minima and help particles to search global optimum with improved convergence. The performance of the newly developed algorithm is verified using various standard multimodal, multivariable, shifted hybrid composition benchmark problems. Further, the comparative analysis of HSPSO with variants of PSO is tested to control frequency of hybrid renewable energy system which comprises solar system, wind system, diesel generator, aqua electrolyzer and ultra capacitor. A significant improvement in convergence characteristic of HSPSO algorithm over other variants of PSO is observed in solving benchmark optimization and renewable hybrid system problems.

  13. Cooperative Quantum-Behaved Particle Swarm Optimization with Dynamic Varying Search Areas and Lévy Flight Disturbance

    PubMed Central

    Li, Desheng

    2014-01-01

    This paper proposes a novel variant of cooperative quantum-behaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSO-DVSA-LFD. One mechanism is called Dynamic Varying Search Area (DVSA), which takes charge of limiting the ranges of particles' activity into a reduced area. On the other hand, in order to escape the local optima, Lévy flights are used to generate the stochastic disturbance in the movement of particles. To test the performance of CQPSO-DVSA-LFD, numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on both benchmark test functions and the combinatorial optimization issue, that is, the job-shop scheduling problem. PMID:24851085

  14. A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

    PubMed Central

    Li, Xuejun; Xu, Jia; Yang, Yun

    2015-01-01

    Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts. PMID:26357510

  15. Multivariable wavelet finite element-based vibration model for quantitative crack identification by using particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Zhang, Xingwu; Gao, Robert X.; Yan, Ruqiang; Chen, Xuefeng; Sun, Chuang; Yang, Zhibo

    2016-08-01

    Crack is one of the crucial causes of structural failure. A methodology for quantitative crack identification is proposed in this paper based on multivariable wavelet finite element method and particle swarm optimization. First, the structure with crack is modeled by multivariable wavelet finite element method (MWFEM) so that the vibration parameters of the first three natural frequencies in arbitrary crack conditions can be obtained, which is named as the forward problem. Second, the structure with crack is tested to obtain the vibration parameters of first three natural frequencies by modal testing and advanced vibration signal processing method. Then, the analyzed and measured first three natural frequencies are combined together to obtain the location and size of the crack by using particle swarm optimization. Compared with traditional wavelet finite element method, MWFEM method can achieve more accurate vibration analysis results because it interpolates all the solving variables at one time, which makes the MWFEM-based method to improve the accuracy in quantitative crack identification. In the end, the validity and superiority of the proposed method are verified by experiments of both cantilever beam and simply supported beam.

  16. Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques

    NASA Astrophysics Data System (ADS)

    Lahmiri, Salim

    2016-02-01

    Multiresolution analysis techniques including continuous wavelet transform, empirical mode decomposition, and variational mode decomposition are tested in the context of interest rate next-day variation prediction. In particular, multiresolution analysis techniques are used to decompose interest rate actual variation and feedforward neural network for training and prediction. Particle swarm optimization technique is adopted to optimize its initial weights. For comparison purpose, autoregressive moving average model, random walk process and the naive model are used as main reference models. In order to show the feasibility of the presented hybrid models that combine multiresolution analysis techniques and feedforward neural network optimized by particle swarm optimization, we used a set of six illustrative interest rates; including Moody's seasoned Aaa corporate bond yield, Moody's seasoned Baa corporate bond yield, 3-Month, 6-Month and 1-Year treasury bills, and effective federal fund rate. The forecasting results show that all multiresolution-based prediction systems outperform the conventional reference models on the criteria of mean absolute error, mean absolute deviation, and root mean-squared error. Therefore, it is advantageous to adopt hybrid multiresolution techniques and soft computing models to forecast interest rate daily variations as they provide good forecasting performance.

  17. Sub-pixel flood inundation mapping from multispectral remotely sensed images based on discrete particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Li, Linyi; Chen, Yun; Yu, Xin; Liu, Rui; Huang, Chang

    2015-03-01

    The study of flood inundation is significant to human life and social economy. Remote sensing technology has provided an effective way to study the spatial and temporal characteristics of inundation. Remotely sensed images with high temporal resolutions are widely used in mapping inundation. However, mixed pixels do exist due to their relatively low spatial resolutions. One of the most popular approaches to resolve this issue is sub-pixel mapping. In this paper, a novel discrete particle swarm optimization (DPSO) based sub-pixel flood inundation mapping (DPSO-SFIM) method is proposed to achieve an improved accuracy in mapping inundation at a sub-pixel scale. The evaluation criterion for sub-pixel inundation mapping is formulated. The DPSO-SFIM algorithm is developed, including particle discrete encoding, fitness function designing and swarm search strategy. The accuracy of DPSO-SFIM in mapping inundation at a sub-pixel scale was evaluated using Landsat ETM + images from study areas in Australia and China. The results show that DPSO-SFIM consistently outperformed the four traditional SFIM methods in these study areas. A sensitivity analysis of DPSO-SFIM was also carried out to evaluate its performances. It is hoped that the results of this study will enhance the application of medium-low spatial resolution images in inundation detection and mapping, and thereby support the ecological and environmental studies of river basins.

  18. A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

    PubMed

    Li, Xuejun; Xu, Jia; Yang, Yun

    2015-01-01

    Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts. PMID:26357510

  19. Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

    PubMed Central

    Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

    2015-01-01

    In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature. PMID:25799141

  20. Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

    PubMed

    Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

    2015-01-01

    In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature. PMID:25799141

  1. Application of Modified Particle Swarm Optimization Method for Parameter Extraction of 2-D TEC Mapping

    NASA Astrophysics Data System (ADS)

    Toker, C.; Gokdag, Y. E.; Arikan, F.; Arikan, O.

    2012-04-01

    Ionosphere is a very important part of Space Weather. Modeling and monitoring of ionospheric variability is a major part of satellite communication, navigation and positioning systems. Total Electron Content (TEC), which is defined as the line integral of the electron density along a ray path, is one of the parameters to investigate the ionospheric variability. Dual-frequency GPS receivers, with their world wide availability and efficiency in TEC estimation, have become a major source of global and regional TEC modeling. When Global Ionospheric Maps (GIM) of International GPS Service (IGS) centers (http://iono.jpl.nasa.gov/gim.html) are investigated, it can be observed that regional ionosphere along the midlatitude regions can be modeled as a constant, linear or a quadratic surface. Globally, especially around the magnetic equator, the TEC surfaces resemble twisted and dispersed single centered or double centered Gaussian functions. Particle Swarm Optimization (PSO) proved itself as a fast converging and an effective optimization tool in various diverse fields. Yet, in order to apply this optimization technique into TEC modeling, the method has to be modified for higher efficiency and accuracy in extraction of geophysical parameters such as model parameters of TEC surfaces. In this study, a modified PSO (mPSO) method is applied to regional and global synthetic TEC surfaces. The synthetic surfaces that represent the trend and small scale variability of various ionospheric states are necessary to compare the performance of mPSO over number of iterations, accuracy in parameter estimation and overall surface reconstruction. The Cramer-Rao bounds for each surface type and model are also investigated and performance of mPSO are tested with respect to these bounds. For global models, the sample points that are used in optimization are obtained using IGS receiver network. For regional TEC models, regional networks such as Turkish National Permanent GPS Network (TNPGN

  2. Comparative Study on Synthesizing Reconfigurable Time- Modulated Linear Arrays using Differential Evolution, Artificial Bee Colony and Particle Swarm Optimization

    NASA Astrophysics Data System (ADS)

    Mandal, S. K.; Singh, Harshavardhan; Mahanti, G. K.; Ghatak, Rowdra

    2014-10-01

    This paper presents a new technique based on optimization tools to design phase only, digitally controlled, reconfigurable antenna arrays through time modulation. In the proposed approach, the on-time durations of the time-modulated elements and the static amplitudes of the array elements are perturbed in such a way that the same on-time sequence and discrete values of static amplitudes for four bit digital attenuators produces either a pencil or a flat-top beam pattern, depending on the suitable discrete phase distributions of five bit digital phase shifters. In order to illustrate the technique, three optimization tools: differential evolution (DE), artificial bee colony (ABC), and particle swarm optimization (PSO) are employed and their performances are compared. The numerical results for a 20-element linear array are presented.

  3. Comparison of Artificial Immune System and Particle Swarm Optimization Techniques for Error Optimization of Machine Vision Based Tool Movements

    NASA Astrophysics Data System (ADS)

    Mahapatra, Prasant Kumar; Sethi, Spardha; Kumar, Amod

    2015-10-01

    In conventional tool positioning technique, sensors embedded in the motion stages provide the accurate tool position information. In this paper, a machine vision based system and image processing technique for motion measurement of lathe tool from two-dimensional sequential images captured using charge coupled device camera having a resolution of 250 microns has been described. An algorithm was developed to calculate the observed distance travelled by the tool from the captured images. As expected, error was observed in the value of the distance traversed by the tool calculated from these images. Optimization of errors due to machine vision system, calibration, environmental factors, etc. in lathe tool movement was carried out using two soft computing techniques, namely, artificial immune system (AIS) and particle swarm optimization (PSO). The results show better capability of AIS over PSO.

  4. Co-combustion of peanut hull and coal blends: Artificial neural networks modeling, particle swarm optimization and Monte Carlo simulation.

    PubMed

    Buyukada, Musa

    2016-09-01

    Co-combustion of coal and peanut hull (PH) were investigated using artificial neural networks (ANN), particle swarm optimization, and Monte Carlo simulation as a function of blend ratio, heating rate, and temperature. The best prediction was reached by ANN61 multi-layer perception model with a R(2) of 0.99994. Blend ratio of 90 to 10 (PH to coal, wt%), temperature of 305°C, and heating rate of 49°Cmin(-1) were determined as the optimum input values and yield of 87.4% was obtained under PSO optimized conditions. The validation experiments resulted in yields of 87.5%±0.2 after three replications. Monte Carlo simulations were used for the probabilistic assessments of stochastic variability and uncertainty associated with explanatory variables of co-combustion process. PMID:27243606

  5. Identification of inelastic parameters based on deep drawing forming operations using a global-local hybrid Particle Swarm approach

    NASA Astrophysics Data System (ADS)

    Vaz, Miguel; Luersen, Marco A.; Muñoz-Rojas, Pablo A.; Trentin, Robson G.

    2016-04-01

    Application of optimization techniques to the identification of inelastic material parameters has substantially increased in recent years. The complex stress-strain paths and high nonlinearity, typical of this class of problems, require the development of robust and efficient techniques for inverse problems able to account for an irregular topography of the fitness surface. Within this framework, this work investigates the application of the gradient-based Sequential Quadratic Programming method, of the Nelder-Mead downhill simplex algorithm, of Particle Swarm Optimization (PSO), and of a global-local PSO-Nelder-Mead hybrid scheme to the identification of inelastic parameters based on a deep drawing operation. The hybrid technique has shown to be the best strategy by combining the good PSO performance to approach the global minimum basin of attraction with the efficiency demonstrated by the Nelder-Mead algorithm to obtain the minimum itself.

  6. Particle swarm optimization algorithm based parameters estimation and control of epileptiform spikes in a neural mass model.

    PubMed

    Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan

    2016-07-01

    This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design. PMID:27475078

  7. Particle swarm optimization algorithm based parameters estimation and control of epileptiform spikes in a neural mass model

    NASA Astrophysics Data System (ADS)

    Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan

    2016-07-01

    This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design.

  8. Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification

    PubMed Central

    Ramyachitra, D.; Sofia, M.; Manikandan, P.

    2015-01-01

    Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions. PMID:26484222

  9. An effective hybrid approach of gene selection and classification for microarray data based on clustering and particle swarm optimization.

    PubMed

    Han, Fei; Yang, Shanxiu; Guan, Jian

    2015-01-01

    In this paper, a hybrid approach based on clustering and Particle Swarm Optimisation (PSO) is proposed to perform gene selection and classification for microarray data. In the new method, firstly, genes are partitioned into a predetermined number of clusters by K-means method. Since the genes in each cluster have much redundancy, Max-Relevance Min-Redundancy (mRMR) strategy is used to reduce redundancy of the clustered genes. Then, PSO is used to perform further gene selection from the remaining clustered genes. Because of its better generalisation performance with much faster convergence rate than other learning algorithms for neural networks, Extreme Learning Machine (ELM) is chosen to evaluate candidate gene subsets selected by PSO and perform samples classification in this study. The proposed method selects less redundant genes as well as increases prediction accuracy and its efficiency and effectiveness are verified by extensive comparisons with other classical methods on three open microarray data. PMID:26547970

  10. Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data.

    PubMed

    Shen, Qi; Shi, Wei-Min; Kong, Wei

    2008-02-01

    Gene expression data are characterized by thousands even tens of thousands of measured genes on only a few tissue samples. This can lead either to possible overfitting and dimensional curse or even to a complete failure in analysis of microarray data. Gene selection is an important component for gene expression-based tumor classification systems. In this paper, we develop a hybrid particle swarm optimization (PSO) and tabu search (HPSOTS) approach for gene selection for tumor classification. The incorporation of tabu search (TS) as a local improvement procedure enables the algorithm HPSOTS to overleap local optima and show satisfactory performance. The proposed approach is applied to three different microarray data sets. Moreover, we compare the performance of HPSOTS on these datasets to that of stepwise selection, the pure TS and PSO algorithm. It has been demonstrated that the HPSOTS is a useful tool for gene selection and mining high dimension data. PMID:18093877

  11. Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification.

    PubMed

    Ramyachitra, D; Sofia, M; Manikandan, P

    2015-09-01

    Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions. PMID:26484222

  12. Side scan sonar image segmentation based on neutrosophic set and quantum-behaved particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Zhao, Jianhu; Wang, Xiao; Zhang, Hongmei; Hu, Jun; Jian, Xiaomin

    2016-06-01

    To fulfill side scan sonar (SSS) image segmentation accurately and efficiently, a novel segmentation algorithm based on neutrosophic set (NS) and quantum-behaved particle swarm optimization (QPSO) is proposed in this paper. Firstly, the neutrosophic subset images are obtained by transforming the input image into the NS domain. Then, a co-occurrence matrix is accurately constructed based on these subset images, and the entropy of the gray level image is described to serve as the fitness function of the QPSO algorithm. Moreover, the optimal two-dimensional segmentation threshold vector is quickly obtained by QPSO. Finally, the contours of the interested target are segmented with the threshold vector and extracted by the mathematic morphology operation. To further improve the segmentation efficiency, the single threshold segmentation, an alternative algorithm, is recommended for the shadow segmentation by considering the gray level characteristics of the shadow. The accuracy and efficiency of the proposed algorithm are assessed with experiments of SSS image segmentation.

  13. Design optimization of long period waveguide grating devices for refractive index sensing using adaptive particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Semwal, Girish; Rastogi, Vipul

    2016-01-01

    Grating assisted surface plasmon resonance waveguide grating has been designed and optimized for the sensing application. Adaptive particle swarm optimization in conjunction with derivative free method for mode computation has been used for design optimization of LPWG sensor. Effect of metal thickness and cladding layer thickness on the core mode and surface plasmon mode has been analyzed in detail. Results have been utilized as benchmarks for deciding the bounds of these variables in the optimization process. Two waveguides structures have been demonstrated for the grating assisted surface plasmon resonance refractive index sensor. The sensitivity of the designed sensors has been achieved 3.5×104 nm/RIU and 5.0×104 nm/RIU with optimized waveguide and grating parameters.

  14. Structural exploration and properties of (H2O)4+ cluster via ab initio in combination with particle swarm optimization method

    NASA Astrophysics Data System (ADS)

    Lv, Zhen-Long; Cheng, Yan; Chen, Xiang-Rong; Cai, Ling-Cang

    2015-05-01

    As a microscopic model to study ionized water, cationic water clusters are hot research subjects in these days, which also has many unique properties compared with their neutral counterparts. Here, the isomers of (H2O)4+ cluster were searched by using particle swarm optimization method with the help of quantum chemical calculations. Eighteen stable candidates were obtained after optimization performed at the MP2/aug-cc-pVDZ level. Their relative Gibbs free energies below 350 K, the infrared spectra of the five lowest energy isomers and the electronic characteristics of the representative isomers were investigated. For these isomers, the effect of the zero point vibrational energies, the relationship between the schemes of the isomers and their energies, and the constituents of the most important orbitals were studied, which provide us with much information for further studying this kind of clusters.

  15. RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

    PubMed

    Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

    2012-09-01

    In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. PMID:22738782

  16. An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink

    NASA Astrophysics Data System (ADS)

    Hu, Yifan; Ding, Yongsheng; Hao, Kuangrong; Ren, Lihong; Han, Hua

    2014-03-01

    The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.

  17. Convergence analysis of particle swarm optimization (PSO) method on the with-in host dengue infection treatment model

    NASA Astrophysics Data System (ADS)

    Handayani, D.; Nuraini, N.; Tse, O.; Saragih, R.; Naiborhu, J.

    2016-04-01

    PSO is a computational optimization method motivated by the social behavior of organisms like bird flocking, fish schooling and human social relations. PSO is one of the most important swarm intelligence algorithms. In this study, we analyze the convergence of PSO when it is applied to with-in host dengue infection treatment model simulation in our early research. We used PSO method to construct the initial adjoin equation and to solve a control problem. Its properties of control input on the continuity of objective function and ability of adapting to the dynamic environment made us have to analyze the convergence of PSO. With the convergence analysis of PSO we will have some parameters that ensure the convergence result of numerical simulations on this model using PSO.

  18. Benchmark calculations of nonconservative charged-particle swarms in dc electric and magnetic fields crossed at arbitrary angles.

    PubMed

    Dujko, S; White, R D; Petrović, Z Lj; Robson, R E

    2010-04-01

    A multiterm solution of the Boltzmann equation has been developed and used to calculate transport coefficients of charged-particle swarms in gases under the influence of electric and magnetic fields crossed at arbitrary angles when nonconservative collisions are present. The hierarchy resulting from a spherical-harmonic decomposition of the Boltzmann equation in the hydrodynamic regime is solved numerically by representing the speed dependence of the phase-space distribution function in terms of an expansion in Sonine polynomials about a Maxwellian velocity distribution at an internally determined temperature. Results are given for electron swarms in certain collisional models for ionization and attachment over a range of angles between the fields and field strengths. The implicit and explicit effects of ionization and attachment on the electron-transport coefficients are considered using physical arguments. It is found that the difference between the two sets of transport coefficients, bulk and flux, resulting from the explicit effects of nonconservative collisions, can be controlled either by the variation in the magnetic field strengths or by the angles between the fields. In addition, it is shown that the phenomena of ionization cooling and/or attachment cooling/heating previously reported for dc electric fields carry over directly to the crossed electric and magnetic fields. The results of the Boltzmann equation analysis are compared with those obtained by a Monte Carlo simulation technique. The comparison confirms the theoretical basis and numerical integrity of the moment method for solving the Boltzmann equation and gives a set of well-established data that can be used to test future codes and plasma models. PMID:20481843

  19. SU-E-T-259: Particle Swarm Optimization in Radial Dose Function Fitting for a Novel Iodine-125 Seed

    SciTech Connect

    Wu, X; Duan, J; Popple, R; Huang, M; Shen, S; Brezovich, I; Cardan, R; Benhabib, S

    2014-06-01

    Purpose: To determine the coefficients of bi- and tri-exponential functions for the best fit of radial dose functions of the new iodine brachytherapy source: Iodine-125 Seed AgX-100. Methods: The particle swarm optimization (PSO) method was used to search for the coefficients of the biand tri-exponential functions that yield the best fit to data published for a few selected radial distances from the source. The coefficients were encoded into particles, and these particles move through the search space by following their local and global best-known positions. In each generation, particles were evaluated through their fitness function and their positions were changed through their velocities. This procedure was repeated until the convergence criterion was met or the maximum generation was reached. All best particles were found in less than 1,500 generations. Results: For the I-125 seed AgX-100 considered as a point source, the maximum deviation from the published data is less than 2.9% for bi-exponential fitting function and 0.2% for tri-exponential fitting function. For its line source, the maximum deviation is less than 1.1% for bi-exponential fitting function and 0.08% for tri-exponential fitting function. Conclusion: PSO is a powerful method in searching coefficients for bi-exponential and tri-exponential fitting functions. The bi- and tri-exponential models of Iodine-125 seed AgX-100 point and line sources obtained with PSO optimization provide accurate analytical forms of the radial dose function. The tri-exponential fitting function is more accurate than the bi-exponential function.

  20. Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach

    PubMed Central

    Cakar, Tarik; Koker, Rasit

    2015-01-01

    A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system. PMID:26221134

  1. Adaptive Particle Swarm Optimizer with Varying Acceleration Coefficients for Finding the Most Stable Conformer of Small Molecules.

    PubMed

    Agrawal, Shikha; Silakari, Sanjay; Agrawal, Jitendra

    2015-11-01

    A novel parameter automation strategy for Particle Swarm Optimization called APSO (Adaptive PSO) is proposed. The algorithm is designed to efficiently control the local search and convergence to the global optimum solution. Parameters c1 controls the impact of the cognitive component on the particle trajectory and c2 controls the impact of the social component. Instead of fixing the value of c1 and c2 , this paper updates the value of these acceleration coefficients by considering time variation of evaluation function along with varying inertia weight factor in PSO. Here the maximum and minimum value of evaluation function is use to gradually decrease and increase the value of c1 and c2 respectively. Molecular energy minimization is one of the most challenging unsolved problems and it can be formulated as a global optimization problem. The aim of the present paper is to investigate the effect of newly developed APSO on the highly complex molecular potential energy function and to check the efficiency of the proposed algorithm to find the global minimum of the function under consideration. The proposed algorithm APSO is therefore applied in two cases: Firstly, for the minimization of a potential energy of small molecules with up to 100 degrees of freedom and finally for finding the global minimum energy conformation of 1,2,3-trichloro-1-flouro-propane molecule based on a realistic potential energy function. The computational results of all the cases show that the proposed method performs significantly better than the other algorithms. PMID:27491033

  2. An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions

    PubMed Central

    Shang, Junliang; Sun, Yan; Li, Shengjun; Liu, Jin-Xing; Zheng, Chun-Hou; Zhang, Junying

    2015-01-01

    SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions. PMID:26236727

  3. Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach.

    PubMed

    Cakar, Tarik; Koker, Rasit

    2015-01-01

    A particle swarm optimization algorithm (PSO) has been used to solve the single machine total weighted tardiness problem (SMTWT) with unequal release date. To find the best solutions three different solution approaches have been used. To prepare subhybrid solution system, genetic algorithms (GA) and simulated annealing (SA) have been used. In the subhybrid system (GA and SA), GA obtains a solution in any stage, that solution is taken by SA and used as an initial solution. When SA finds better solution than this solution, it stops working and gives this solution to GA again. After GA finishes working the obtained solution is given to PSO. PSO searches for better solution than this solution. Later it again sends the obtained solution to GA. Three different solution systems worked together. Neurohybrid system uses PSO as the main optimizer and SA and GA have been used as local search tools. For each stage, local optimizers are used to perform exploitation to the best particle. In addition to local search tools, neurodominance rule (NDR) has been used to improve performance of last solution of hybrid-PSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybrid-PSO solution system. PMID:26221134

  4. Inverse 4D conformal planning for lung SBRT using particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Modiri, A.; Gu, X.; Hagan, A.; Bland, R.; Iyengar, P.; Timmerman, R.; Sawant, A.

    2016-08-01

    A critical aspect of highly potent regimens such as lung stereotactic body radiation therapy (SBRT) is to avoid collateral toxicity while achieving planning target volume (PTV) coverage. In this work, we describe four dimensional conformal radiotherapy using a highly parallelizable swarm intelligence-based stochastic optimization technique. Conventional lung CRT-SBRT uses a 4DCT to create an internal target volume and then, using forward-planning, generates a 3D conformal plan. In contrast, we investigate an inverse-planning strategy that uses 4DCT data to create a 4D conformal plan, which is optimized across the three spatial dimensions (3D) as well as time, as represented by the respiratory phase. The key idea is to use respiratory motion as an additional degree of freedom. We iteratively adjust fluence weights for all beam apertures across all respiratory phases considering OAR sparing, PTV coverage and delivery efficiency. To demonstrate proof-of-concept, five non-small-cell lung cancer SBRT patients were retrospectively studied. The 4D optimized plans achieved PTV coverage comparable to the corresponding clinically delivered plans while showing significantly superior OAR sparing ranging from 26% to 83% for D max heart, 10%–41% for D max esophagus, 31%–68% for D max spinal cord and 7%–32% for V 13 lung.

  5. Inverse 4D conformal planning for lung SBRT using particle swarm optimization.

    PubMed

    Modiri, A; Gu, X; Hagan, A; Bland, R; Iyengar, P; Timmerman, R; Sawant, A

    2016-08-21

    A critical aspect of highly potent regimens such as lung stereotactic body radiation therapy (SBRT) is to avoid collateral toxicity while achieving planning target volume (PTV) coverage. In this work, we describe four dimensional conformal radiotherapy using a highly parallelizable swarm intelligence-based stochastic optimization technique. Conventional lung CRT-SBRT uses a 4DCT to create an internal target volume and then, using forward-planning, generates a 3D conformal plan. In contrast, we investigate an inverse-planning strategy that uses 4DCT data to create a 4D conformal plan, which is optimized across the three spatial dimensions (3D) as well as time, as represented by the respiratory phase. The key idea is to use respiratory motion as an additional degree of freedom. We iteratively adjust fluence weights for all beam apertures across all respiratory phases considering OAR sparing, PTV coverage and delivery efficiency. To demonstrate proof-of-concept, five non-small-cell lung cancer SBRT patients were retrospectively studied. The 4D optimized plans achieved PTV coverage comparable to the corresponding clinically delivered plans while showing significantly superior OAR sparing ranging from 26% to 83% for D max heart, 10%-41% for D max esophagus, 31%-68% for D max spinal cord and 7%-32% for V 13 lung. PMID:27476472

  6. Self-Organizing Hierarchical Particle Swarm Optimization with Time-Varying Acceleration Coefficients for Economic Dispatch with Valve Point Effects and Multifuel Options

    NASA Astrophysics Data System (ADS)

    Polprasert, Jirawadee; Ongsakul, Weerakorn; Dieu, Vo Ngoc

    2011-06-01

    This paper proposes a self-organizing hierarchical particle swarm optimization (SPSO) with time-varying acceleration coefficients (TVAC) for solving economic dispatch (ED) problem with non-smooth functions including multiple fuel options (MFO) and valve-point loading effects (VPLE). The proposed SPSO with TVAC is the new approach optimizer and good performance for solving ED problems. It can handle the premature convergence of the problem by re-initialization of velocity whenever particles are stagnated in the search space. To properly control both local and global explorations of the swarm during the optimization process, the performance of TVAC is included. The proposed method is tested in different ED problems with non-smooth cost functions and the obtained results are compared to those from many other methods in the literature. The results have revealed that the proposed SPSO with TVAC is effective in finding higher quality solutions for non-smooth ED problems than many other methods.

  7. Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines

    NASA Astrophysics Data System (ADS)

    Taormina, Riccardo; Chau, Kwok-Wing

    2015-10-01

    Selecting an adequate set of inputs is a critical step for successful data-driven streamflow prediction. In this study, we present a novel approach for Input Variable Selection (IVS) that employs Binary-coded discrete Fully Informed Particle Swarm optimization (BFIPS) and Extreme Learning Machines (ELM) to develop fast and accurate IVS algorithms. A scheme is employed to encode the subset of selected inputs and ELM specifications into the binary particles, which are evolved using single objective and multi-objective BFIPS optimization (MBFIPS). The performances of these ELM-based methods are assessed using the evaluation criteria and the datasets included in the comprehensive IVS evaluation framework proposed by Galelli et al. (2014). From a comparison with 4 major IVS techniques used in their original study it emerges that the proposed methods compare very well in terms of selection accuracy. The best performers were found to be (1) a MBFIPS-ELM algorithm based on the concurrent minimization of an error function and the number of selected inputs, and (2) a BFIPS-ELM algorithm based on the minimization of a variant of the Akaike Information Criterion (AIC). The first technique is arguably the most accurate overall, and is able to reach an almost perfect specification of the optimal input subset for a partially synthetic rainfall-runoff experiment devised for the Kentucky River basin. In addition, MBFIPS-ELM allows for the determination of the relative importance of the selected inputs. On the other hand, the BFIPS-ELM is found to consistently reach high accuracy scores while being considerably faster. By extrapolating the results obtained on the IVS test-bed, it can be concluded that the proposed techniques are particularly suited for rainfall-runoff modeling applications characterized by high nonlinearity in the catchment dynamics.

  8. A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

    PubMed Central

    Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

    2016-01-01

    In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides. PMID:27187430

  9. A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.

    PubMed

    Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

    2016-01-01

    In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides. PMID:27187430

  10. Optimal design of HTS magnets for a modular toroid-type 2.5 MJ SMES using multi-grouped particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Lee, S. Y.; Kwak, S. Y.; Seo, J. H.; Lee, S. Y.; Park, S. H.; Kim, W. S.; Lee, J. K.; Bae, J. H.; Kim, S. H.; Sim, K. D.; Seong, K. C.; Jung, H. K.; Choi, K.; Hahn, S.

    2009-10-01

    Superconducting magnetic energy storage (SMES) is one of the promising power system applications of superconducting technology and has been actively researched and developed worldwide. Generally, there are three types of SMES-solenoid, multiple solenoid, and toroid. Among these types, toroid type seems to require more wires than solenoid type and multiple solenoid type at the same operating current. However toroid type reduces normal field in the wire and stray field dramatically because magnetic field is confined inside the coil. So, the total length of wire in the toroid type can be reduced in comparison with that in the solenoid type by increasing operating current. In this paper, a 2.5 MJ class SMES with HTS magnets of single solenoid, multiple solenoid and modular toroid type were optimized using a recently developed multi-modal optimization technique named multi-grouped particle swarm optimization (MGPSO). The objective of the optimization was to minimize the total length of HTS superconductor wires satisfying some equality and inequality constraints. The stored energy and constraints were calculated using 3D magnetic field analysis techniques and an automatic tetrahedral mesh generator. Optimized results were verified by 3D finite element method (FEM).

  11. Multi-Objective Parallel Test-Sheet Composition Using Enhanced Particle Swarm Optimization

    ERIC Educational Resources Information Center

    Ho, Tsu-Feng; Yin, Peng-Yeng; Hwang, Gwo-Jen; Shyu, Shyong Jian; Yean, Ya-Nan

    2009-01-01

    For large-scale tests, such as certification tests or entrance examinations, the composed test sheets must meet multiple assessment criteria. Furthermore, to fairly compare the knowledge levels of the persons who receive tests at different times owing to the insufficiency of available examination halls or the occurrence of certain unexpected…

  12. Robot Swarms

    NASA Technical Reports Server (NTRS)

    Morring, Frank, Jr.

    2005-01-01

    Engineers and interns at this NASA field center are building the prototype of a robotic rover that could go where no wheeled rover has gone before-into the dark cold craters at the lunar poles and across the Moon s rugged highlands-like a walking tetrahedron. With NASA pushing to meet President Bush's new exploration objectives, the robots taking shape here today could be on the Moon in a decade. In the longer term, the concept could lead to shape-shifting robot swarms designed to explore distant planetary surfaces in advance of humans. "If you look at all of NASA s projections of the future, anyone s projections of the space program, they re all rigid-body architecture," says Steven Curtis, principal investigator on the effort. "This is not rigid-body. The whole key here is flexibility and reconfigurability with a capital R."

  13. Colloidal Swarms Can Settle Faster than Isolated Particles: Enhanced Sedimentation near Phase Separation

    NASA Astrophysics Data System (ADS)

    Lattuada, Enrico; Buzzaccaro, Stefano; Piazza, Roberto

    2016-01-01

    By experimenting on model colloids where depletion forces can be carefully tuned and quantified, we show that attractive interactions consistently "promote" particle settling, so much that the sedimentation velocity of a moderately concentrated dispersion can even exceed its single-particle value. At larger particle volume fraction ϕ , however, hydrodynamic hindrance eventually takes over. Hence, v (ϕ ) actually displays a nonmonotonic trend that may threaten the stability of the settling front to thermal perturbations. Finally, by discussing a representative case, we show that these results are relevant to the investigation of protein association effects by ultracentrifugation.

  14. Colloidal Swarms Can Settle Faster than Isolated Particles: Enhanced Sedimentation near Phase Separation.

    PubMed

    Lattuada, Enrico; Buzzaccaro, Stefano; Piazza, Roberto

    2016-01-22

    By experimenting on model colloids where depletion forces can be carefully tuned and quantified, we show that attractive interactions consistently "promote" particle settling, so much that the sedimentation velocity of a moderately concentrated dispersion can even exceed its single-particle value. At larger particle volume fraction ϕ, however, hydrodynamic hindrance eventually takes over. Hence, v(ϕ) actually displays a nonmonotonic trend that may threaten the stability of the settling front to thermal perturbations. Finally, by discussing a representative case, we show that these results are relevant to the investigation of protein association effects by ultracentrifugation. PMID:26849617

  15. Systems for detecting charged particles in object inspection

    SciTech Connect

    Morris, Christopher L.; Makela, Mark F.

    2013-08-20

    Techniques, apparatus and systems for detecting particles such as muons. In one implementation, a monitoring system has a cosmic ray-produced charged particle tracker with a plurality of drift cells. The drift cells, which can be for example aluminum drift tubes, can be arranged at least above and below a volume to be scanned to thereby track incoming and outgoing charged particles, such as cosmic ray-produced muons, while also detecting gamma rays. The system can selectively detect devices or materials, such as iron, lead, gold and/or tungsten, occupying the volume from multiple scattering of the charged particles passing through the volume and can also detect any radioactive sources occupying the volume from gamma rays emitted therefrom. If necessary, the drift tubes can be sealed to eliminate the need for a gas handling system. The system can be employed to inspect occupied vehicles at border crossings for nuclear threat objects.

  16. Autonomous and Autonomic Swarms

    NASA Technical Reports Server (NTRS)

    Hinchey, Michael G.; Rash, James L.; Truszkowski, Walter F.; Rouff, Christopher A.; Sterritt, Roy

    2005-01-01

    A watershed in systems engineering is represented by the advent of swarm-based systems that accomplish missions through cooperative action by a (large) group of autonomous individuals each having simple capabilities and no global knowledge of the group s objective. Such systems, with individuals capable of surviving in hostile environments, pose unprecedented challenges to system developers. Design and testing and verification at much higher levels will be required, together with the corresponding tools, to bring such systems to fruition. Concepts for possible future NASA space exploration missions include autonomous, autonomic swarms. Engineering swarm-based missions begins with understanding autonomy and autonomicity and how to design, test, and verify systems that have those properties and, simultaneously, the capability to accomplish prescribed mission goals. Formal methods-based technologies, both projected and in development, are described in terms of their potential utility to swarm-based system developers.

  17. Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce.

    PubMed

    Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

    2016-01-01

    A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987

  18. An optimized watermarking scheme using an encrypted gyrator transform computer generated hologram based on particle swarm optimization.

    PubMed

    Li, Jianzhong

    2014-04-21

    In this paper, a novel secure optimal image watermarking scheme using an encrypted gyrator transform computer generated hologram (CGH) in the contourlet domain is presented. A new encrypted CGH technique, which is based on the gyrator transform, the random phase mask, the three-step phase-shifting interferometry and the Fibonacci transform, has been proposed to produce a hologram of a watermark first. With the huge key space of the encrypted CGH, the security strength of the watermarking system is enhanced. To achieve better imperceptibility, an improved quantization embedding algorithm is proposed to embed the encrypted CGH into the low frequency sub-band of the contourlet-transformed host image. In order to obtain the highest possible robustness without losing the imperceptibility, particle swarm optimization algorithm is employed to search the optimal embedding parameter of the watermarking system. In comparison with other method, the proposed watermarking scheme offers better performances for both imperceptibility and robustness. Experimental results demonstrate that the proposed image watermarking is not only secure and invisible, but also robust against a variety of attacks. PMID:24787882

  19. Accuracy and precisions of water quality parameters retrieved from particle swarm optimisation in a sub-tropical lake

    NASA Astrophysics Data System (ADS)

    Campbell, Glenn; Phinn, Stuart R.

    2009-09-01

    Optical remote sensing has been used to map and monitor water quality parameters such as the concentrations of hydrosols (chlorophyll and other pigments, total suspended material, and coloured dissolved organic matter). In the inversion / optimisation approach a forward model is used to simulate the water reflectance spectra from a set of parameters and the set that gives the closest match is selected as the solution. The accuracy of the hydrosol retrieval is dependent on an efficient search of the solution space and the reliability of the similarity measure. In this paper the Particle Swarm Optimisation (PSO) was used to search the solution space and seven similarity measures were trialled. The accuracy and precision of this method depends on the inherent noise in the spectral bands of the sensor being employed, as well as the radiometric corrections applied to images to calculate the subsurface reflectance. Using the Hydrolight® radiative transfer model and typical hydrosol concentrations from Lake Wivenhoe, Australia, MERIS reflectance spectra were simulated. The accuracy and precision of hydrosol concentrations derived from each similarity measure were evaluated after errors associated with the air-water interface correction, atmospheric correction and the IOP measurement were modelled and applied to the simulated reflectance spectra. The use of band specific empirically estimated values for the anisotropy value in the forward model improved the accuracy of hydrosol retrieval. The results of this study will be used to improve an algorithm for the remote sensing of water quality for freshwater impoundments.

  20. A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms

    PubMed Central

    Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei

    2016-01-01

    Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field. PMID:26861348

  1. Temperature drift modeling and compensation of fiber optical gyroscope based on improved support vector machine and particle swarm optimization algorithms.

    PubMed

    Wang, Wei; Chen, Xiyuan

    2016-08-10

    Modeling and compensation of temperature drift is an important method for improving the precision of fiber-optic gyroscopes (FOGs). In this paper, a new method of modeling and compensation for FOGs based on improved particle swarm optimization (PSO) and support vector machine (SVM) algorithms is proposed. The convergence speed and reliability of PSO are improved by introducing a dynamic inertia factor. The regression accuracy of SVM is improved by introducing a combined kernel function with four parameters and piecewise regression with fixed steps. The steps are as follows. First, the parameters of the combined kernel functions are optimized by the improved PSO algorithm. Second, the proposed kernel function of SVM is used to carry out piecewise regression, and the regression model is also obtained. Third, the temperature drift is compensated for by the regression data. The regression accuracy of the proposed method (in the case of mean square percentage error indicators) increased by 83.81% compared to the traditional SVM. PMID:27534465

  2. An example image super-resolution algorithm based on modified k-means with hybrid particle swarm optimization

    NASA Astrophysics Data System (ADS)

    Feng, Kunpeng; Zhou, Tong; Cui, Jiwen; Tan, Jiubin

    2014-11-01

    This paper presents a novel example-based super-resolution (SR) algorithm with improved k-means cluster. In this algorithm, genetic k-means (GKM) with hybrid particle swarm optimization (HPSO) is employed to improve the reconstruction of high-resolution (HR) images, and a pre-processing of classification in frequency is used to accelerate the procedure. Self-redundancy across different scales of a natural image is also utilized to build attached training set to expand example-based information. Meanwhile, a reconstruction algorithm based on hybrid supervise locally linear embedding (HSLLE) is proposed which uses training sets, high-resolution images and self-redundancy across different scales of a natural image. Experimental results show that patches are classified rapidly in training set processing session and the runtime of reconstruction is half of traditional algorithm at least in super-resolution session. And clustering and attached training set lead to a better recovery of low-resolution (LR) image.

  3. Global nonlinear kernel prediction for large data set with a particle swarm-optimized interval support vector regression.

    PubMed

    Ding, Yongsheng; Cheng, Lijun; Pedrycz, Witold; Hao, Kuangrong

    2015-10-01

    A new global nonlinear predictor with a particle swarm-optimized interval support vector regression (PSO-ISVR) is proposed to address three issues (viz., kernel selection, model optimization, kernel method speed) encountered when applying SVR in the presence of large data sets. The novel prediction model can reduce the SVR computing overhead by dividing input space and adaptively selecting the optimized kernel functions to obtain optimal SVR parameter by PSO. To quantify the quality of the predictor, its generalization performance and execution speed are investigated based on statistical learning theory. In addition, experiments using synthetic data as well as the stock volume weighted average price are reported to demonstrate the effectiveness of the developed models. The experimental results show that the proposed PSO-ISVR predictor can improve the computational efficiency and the overall prediction accuracy compared with the results produced by the SVR and other regression methods. The proposed PSO-ISVR provides an important tool for nonlinear regression analysis of big data. PMID:25974954

  4. Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

    PubMed

    Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

    2015-08-01

    Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods. PMID:26737960

  5. MRI Brain Images Healthy and Pathological Tissues Classification with the Aid of Improved Particle Swarm Optimization and Neural Network

    PubMed Central

    Sheejakumari, V.; Sankara Gomathi, B.

    2015-01-01

    The advantages of magnetic resonance imaging (MRI) over other diagnostic imaging modalities are its higher spatial resolution and its better discrimination of soft tissue. In the previous tissues classification method, the healthy and pathological tissues are classified from the MRI brain images using HGANN. But the method lacks sensitivity and accuracy measures. The classification method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new classification method is proposed in this paper. Here, new tissues classification method is proposed with improved particle swarm optimization (IPSO) technique to classify the healthy and pathological tissues from the given MRI images. Our proposed classification method includes the same four stages, namely, tissue segmentation, feature extraction, heuristic feature selection, and tissue classification. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of the proposed classification method in classifying the tissues and the achieved improvement in sensitivity and accuracy measures. Furthermore, the performance of the proposed technique is evaluated by comparing it with the other segmentation methods. PMID:25977706

  6. Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

    PubMed

    Shabri, Ani; Samsudin, Ruhaidah

    2014-01-01

    Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

  7. Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm

    PubMed Central

    2014-01-01

    Background Facial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear. Methods This study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups). Results The analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance. Conclusions This study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion. PMID:24955105

  8. Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

    PubMed Central

    Shabri, Ani; Samsudin, Ruhaidah

    2014-01-01

    Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

  9. Constant optimization of oral drug absorption kinetics in the compartment absorption and transit models using particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Prabowo, K.; Sumaryada, T.; Kartono, A.

    2016-01-01

    Simulation of predictive modeling oral drug namely Compartment Absorption and Transit (CAT) using Particle Swarm Optimization (PSO) algorithm has been performed. This research will be carried out optimization of kinetic constant value oral drug use PSO algorithm to obtain the best global transport constant values for CAT equation that can predict drug concentration in plasma. The value of drug absorption rate constant for drug atenolol 25 mg is k10, k12, k21, k13 and k31 with each value is 0.8562, 0.3736, 0.2191, 0.4334 and 1.000 have been obtained thus raising the value of the coefficient of determination of a model CAT. From the experimental data plasma drug concentrations used are Atenolol, the coefficient of determination (R2) obtained from simulations atenolol 25 mg (PSO) was 81.72% and 99.46%. Better correlation between the dependent variable as the drug concentration and explanatory variables such as mass medication, plasma volume, and rate of absorption of the drug has increased in CAT models using PSO algorithm. Based on the results of CAT models fit charts can predict drug concentration in plasma.

  10. Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce

    PubMed Central

    Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

    2016-01-01

    A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987

  11. Parameter estimation of atmospheric refractivity from radar clutter using the particle swarm optimization via Lévy flight

    NASA Astrophysics Data System (ADS)

    Zhang, Zhi-Hua; Sheng, Zheng; Shi, Han-Qing

    2015-01-01

    Estimating refractivity profiles from radar sea clutter is a complex nonlinear optimization problem. To deal with the ill-posed difficulties, an inversion algorithm, particle swarm optimization with a Lévy flight (LPSO), was proposed to be applied in the refractivity from clutter (RFC) technique to retrieve atmospheric duct in this paper. PSO has many advantages in solving continuous optimization problems, while in its late period it has slow convergence speed and low precision. Therefore, we integrate the Lévy flights into the standard PSO algorithm to improve the precision and enhance the capability of jumping out of the local optima. To verify the feasibility and validation of the LPSO for estimating atmospheric duct parameters based on the RFC method, the synthetic and Wallops98 experimental data are implemented. Numerical experiments demonstrate that the optimal solutions obtained from the hybrid algorithm are more precise and efficient. Additionally, to test the algorithm inversion performance, the antinoise ability of LPSO is analyzed. The results indicate that the LPSO algorithm has a certain antinoise ability. Finally, according to the experiment results, it can be concluded that the LPSO algorithm can provide a more precise and efficient method for near-real-time inversion of atmospheric refractivity from radar clutter.

  12. Object Oriented Design and the Standard Model of particle physics

    NASA Astrophysics Data System (ADS)

    Lipovaca, Samir

    2007-04-01

    Inspired by the computer as both tool and metaphor, a new path emerges toward understanding life, physics, and existence. The path leads throughout all of nature, from the interior of cells to inside black holes. This view of science is based on the idea that information is the ultimate ``substance'' from which all things are made. Exploring this view, we will focus on Object - Oriented (OO) design as one of the most important designs in software development. The OO design views the world as composed of objects with well defined properties. The dynamics is pictured as interactions among objects. Interactions are mediated by messages that objects exchange with each other. This description closely resembles the view of the elementary particles world created by the Standard Model of particle physics. The object model (OM) provides a theoretical foundation upon which the OO design is built. The OM is based on the principles of abstraction, encapsulation, modularity and hierarchy. We will show that the Standard Model of particle physics follows the OM principles.

  13. Multiple states and joint objects particle filter for eye tracking

    NASA Astrophysics Data System (ADS)

    Xiong, Jin; Jiang, Zhaohui; Liu, Junwei; Feng, Huanqing

    2007-11-01

    Recent works have proven that the particle filter is a powerful tracking technique for non-linear and non-Gaussian estimation problem. This paper presents an extension algorithm based on the color-based particle filter framework, which is applicable for complex eye tracking because of two main innovations. Firstly, an employment of an extra discrete-value variable and its associated transition probability matrix (TPM) makes it feasible in tracking multiple states of the eye during blinking. Secondly, the joint-object thought used in state vector eliminates the distraction from eyes to each other. The experimental results illustrate that the proposed algorithm is efficient for eye tracking.

  14. Sound quality prediction of vehicle interior noise and mathematical modeling using a back propagation neural network (BPNN) based on particle swarm optimization (PSO)

    NASA Astrophysics Data System (ADS)

    Zhang, Enlai; Hou, Liang; Shen, Chao; Shi, Yingliang; Zhang, Yaxiang

    2016-01-01

    To better solve the complex non-linear problem between the subjective sound quality evaluation results and objective psychoacoustics parameters, a method for the prediction of the sound quality is put forward by using a back propagation neural network (BPNN) based on particle swarm optimization (PSO), which is optimizing the initial weights and thresholds of BP network neurons through the PSO. In order to verify the effectiveness and accuracy of this approach, the noise signals of the B-Class vehicles from the idle speed to 120 km h-1 measured by the artificial head, are taken as a target. In addition, this paper describes a subjective evaluation experiment on the sound quality annoyance inside the vehicles through a grade evaluation method, by which the annoyance of each sample is obtained. With the use of Artemis software, the main objective psychoacoustic parameters of each noise sample are calculated. These parameters include loudness, sharpness, roughness, fluctuation, tonality, articulation index (AI) and A-weighted sound pressure level. Furthermore, three evaluation models with the same artificial neural network (ANN) structure are built: the standard BPNN model, the genetic algorithm-back-propagation neural network (GA-BPNN) model and the PSO-back-propagation neural network (PSO-BPNN) model. After the network training and the evaluation prediction on the three models’ network based on experimental data, it proves that the PSO-BPNN method can achieve convergence more quickly and improve the prediction accuracy of sound quality, which can further lay a foundation for the control of the sound quality inside vehicles.

  15. A Wiki-Based Teaching Material Development Environment with Enhanced Particle Swarm Optimization

    ERIC Educational Resources Information Center

    Lin, Yen-Ting; Lin, Yi-Chun; Huang, Yueh-Min; Cheng, Shu-Chen

    2013-01-01

    One goal of e-learning is to enhance the interoperability and reusability of learning resources. However, current e-learning systems do little to adequately support this. In order to achieve this aim, the first step is to consider how to assist instructors in re-organizing the existing learning objects. However, when instructors are dealing with a…

  16. Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM.

    PubMed

    Liu, Zhao-Hua; Zhang, Jing; Zhou, Shao-Wu; Li, Xiao-Hua; Liu, Kan

    2013-12-01

    In this paper, a coevolutionary particle-swarmoptimization (PSO) algorithm associating with the artificial immune principle is proposed. In the proposed algorithm, the wholepopulation is divided into two kinds of subpopulations consisting of one elite subpopulation and several normal subpopulations. The best individual of each normal subpopulation will be memorized into the elite subpopulation during the evolution process. A hybrid method, which creates new individuals by using three different operators, is presented to ensure the diversity of all the subpopulations. Furthermore, a simple adaptive wavelet learning operator is utilized for accelerating the convergence speed of the pbest particles. The improved immune-clonal-selection operator is employed for optimizing the elite subpopulation, while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The performance of the proposed algorithm is verified by testing on a suite of standard benchmark functions, which shows faster convergence and global search ability. Its performance is further evaluated by its application to multiparameter estimation of permanentmagnet synchronous machines, which shows that its performance significantly outperforms existing PSOs. The proposed algorithm can estimate the machine dq-axis inductances, stator winding resistance, and rotor flux linkage simultaneously. PMID:23757582

  17. Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.

    PubMed

    Zyout, Imad; Czajkowska, Joanna; Grzegorzek, Marcin

    2015-12-01

    The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multi-scale textural descriptors based on wavelet and gray level co-occurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (mini-MIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyper-parameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on co-occurrence matrix of wavelet image representation technique. PMID:25795630

  18. TH-A-9A-02: BEST IN PHYSICS (THERAPY) - 4D IMRT Planning Using Highly- Parallelizable Particle Swarm Optimization

    SciTech Connect

    Modiri, A; Gu, X; Sawant, A

    2014-06-15

    Purpose: We present a particle swarm optimization (PSO)-based 4D IMRT planning technique designed for dynamic MLC tracking delivery to lung tumors. The key idea is to utilize the temporal dimension as an additional degree of freedom rather than a constraint in order to achieve improved sparing of organs at risk (OARs). Methods: The target and normal structures were manually contoured on each of the ten phases of a 4DCT scan acquired from a lung SBRT patient who exhibited 1.5cm tumor motion despite the use of abdominal compression. Corresponding ten IMRT plans were generated using the Eclipse treatment planning system. These plans served as initial guess solutions for the PSO algorithm. Fluence weights were optimized over the entire solution space i.e., 10 phases × 12 beams × 166 control points. The size of the solution space motivated our choice of PSO, which is a highly parallelizable stochastic global optimization technique that is well-suited for such large problems. A summed fluence map was created using an in-house B-spline deformable image registration. Each plan was compared with a corresponding, internal target volume (ITV)-based IMRT plan. Results: The PSO 4D IMRT plan yielded comparable PTV coverage and significantly higher dose—sparing for parallel and serial OARs compared to the ITV-based plan. The dose-sparing achieved via PSO-4DIMRT was: lung Dmean = 28%; lung V20 = 90%; spinal cord Dmax = 23%; esophagus Dmax = 31%; heart Dmax = 51%; heart Dmean = 64%. Conclusion: Truly 4D IMRT that uses the temporal dimension as an additional degree of freedom can achieve significant dose sparing of serial and parallel OARs. Given the large solution space, PSO represents an attractive, parallelizable tool to achieve globally optimal solutions for such problems. This work was supported through funding from the National Institutes of Health and Varian Medical Systems. Amit Sawant has research funding from Varian Medical Systems, VisionRT Ltd. and Elekta.

  19. Particle Swarm Optimization with Reinforcement Learning for the Prediction of CpG Islands in the Human Genome

    PubMed Central

    Chuang, Li-Yeh; Huang, Hsiu-Chen; Lin, Ming-Cheng; Yang, Cheng-Hong

    2011-01-01

    Background Regions with abundant GC nucleotides, a high CpG number, and a length greater than 200 bp in a genome are often referred to as CpG islands. These islands are usually located in the 5′ end of genes. Recently, several algorithms for the prediction of CpG islands have been proposed. Methodology/Principal Findings We propose here a new method called CPSORL to predict CpG islands, which consists of a complement particle swarm optimization algorithm combined with reinforcement learning to predict CpG islands more reliably. Several CpG island prediction tools equipped with the sliding window technique have been developed previously. However, the quality of the results seems to rely too much on the choices that are made for the window sizes, and thus these methods leave room for improvement. Conclusions/Significance Experimental results indicate that CPSORL provides results of a higher sensitivity and a higher correlation coefficient in all selected experimental contigs than the other methods it was compared to (CpGIS, CpGcluster, CpGProd and CpGPlot). A higher number of CpG islands were identified in chromosomes 21 and 22 of the human genome than with the other methods from the literature. CPSORL also achieved the highest coverage rate (3.4%). CPSORL is an application for identifying promoter and TSS regions associated with CpG islands in entire human genomic. When compared to CpGcluster, the islands predicted by CPSORL covered a larger region in the TSS (12.2%) and promoter (26.1%) region. If Alu sequences are considered, the islands predicted by CPSORL (Alu) covered a larger TSS (40.5%) and promoter (67.8%) region than CpGIS. Furthermore, CPSORL was used to verify that the average methylation density was 5.33% for CpG islands in the entire human genome. PMID:21738602

  20. Comparison of Genetic Algorithm, Particle Swarm Optimization and Biogeography-based Optimization for Feature Selection to Classify Clusters of Microcalcifications

    NASA Astrophysics Data System (ADS)

    Khehra, Baljit Singh; Pharwaha, Amar Partap Singh

    2016-06-01

    Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

  1. Accurate evaluation of free-form surface profile error based on quasi particle swarm optimization algorithm and surface subdivision

    NASA Astrophysics Data System (ADS)

    Wen, Xiulan; Zhao, Yibing; Wang, Dongxia; Zhu, Xiaochu; Xue, Xiaoqiang

    2013-03-01

    Although significant progress has been made in precision machining of free-form surfaces recently, inspection of such surfaces remains a difficult problem. In order to solve the problem that no specific standards for the verification of free-form surface profile are available, the profile parameters of free-form surface are proposed by referring to ISO standards regarding form tolerances and considering its complexity and non-rotational symmetry. Non-uniform rational basis spline(NURBS) for describing free-form surface is formulated. Crucial issues in surface inspection and profile error verification are localization between the design coordinate system(DCS) and measurement coordinate system(MCS) for searching the closest points on the design model corresponding to measured points. A quasi particle swarm optimization(QPSO) is proposed to search the transformation parameters to implement localization between DCS and MCS. Surface subdivide method which does the searching in a recursively reduced range of the parameters u and v of the NURBS design model is developed to find the closest points. In order to verify the effectiveness of the proposed methods, the design model is generated by NURBS and the measurement data of simulation example are generated by transforming the design model to arbitrary position and orientation, and the parts are machined based on the design model and are measured on CMM. The profile errors of simulation example and actual parts are calculated by the proposed method. The results verify that the evaluation precision of freeform surface profile error by the proposed method is higher 10%-22% than that by CMM software. The proposed method deals with the hard problem that it has a lower precision in profile error evaluation of free-form surface.

  2. Is wave-particle objectivity compatible with determinism and locality?

    NASA Astrophysics Data System (ADS)

    Ionicioiu, Radu; Jennewein, Thomas; Mann, Robert B.; Terno, Daniel R.

    2014-09-01

    Wave-particle duality, superposition and entanglement are among the most counterintuitive features of quantum theory. Their clash with our classical expectations motivated hidden-variable (HV) theories. With the emergence of quantum technologies, we can test experimentally the predictions of quantum theory versus HV theories and put strong restrictions on their key assumptions. Here, we study an entanglement-assisted version of the quantum delayed-choice experiment and show that the extension of HV to the controlling devices only exacerbates the contradiction. We compare HV theories that satisfy the conditions of objectivity (a property of photons being either particles or waves, but not both), determinism and local independence of hidden variables with quantum mechanics. Any two of the above conditions are compatible with it. The conflict becomes manifest when all three conditions are imposed and persists for any non-zero value of entanglement. We propose an experiment to test our conclusions.

  3. Is wave-particle objectivity compatible with determinism and locality?

    PubMed

    Ionicioiu, Radu; Jennewein, Thomas; Mann, Robert B; Terno, Daniel R

    2014-01-01

    Wave-particle duality, superposition and entanglement are among the most counterintuitive features of quantum theory. Their clash with our classical expectations motivated hidden-variable (HV) theories. With the emergence of quantum technologies, we can test experimentally the predictions of quantum theory versus HV theories and put strong restrictions on their key assumptions. Here, we study an entanglement-assisted version of the quantum delayed-choice experiment and show that the extension of HV to the controlling devices only exacerbates the contradiction. We compare HV theories that satisfy the conditions of objectivity (a property of photons being either particles or waves, but not both), determinism and local independence of hidden variables with quantum mechanics. Any two of the above conditions are compatible with it. The conflict becomes manifest when all three conditions are imposed and persists for any non-zero value of entanglement. We propose an experiment to test our conclusions. PMID:25256419

  4. Are the Greeks and Trojans Different? --- Comparing the Brightest Objects in Jupiter's L4 and L5 Swarms

    NASA Astrophysics Data System (ADS)

    Chatelain, Joseph P.; Henry, Todd J.; Scott, Nicholas J.; Cartwright, Richard; Emery, Josh

    2011-02-01

    We propose to make UBVRI photometric observations of at least the 113 brightest Jupiter Trojans from both the L4 (Greek) and the L5 (Trojan) Lagrange points using the CTIO 0.9m, in conjunction with data gathered at Lowell Observatory. With these data we hope to reveal any color trends and/or differences between the largest members of the two camps. A comprehensive database of uniform photometry does not exist for this effectively complete sample, so robust comparisons are virtually impossible at this time. These data will also enable comparisons between the Greek and Trojan swarms and other Solar System populations to discover the possible origins of the two camps, which remain surprisingly obscure. Over the course of the program we will collect several important phase curves that can be used to systematically calibrate the photometry over time. During non- photometric conditions, we will measure light curves that yield information about albedo changes (or a lack thereof,which would also be informative), shapes, and rotation periods. The proposed observations will comprise a significant portion of the PI's PhD thesis.

  5. White holes: the extreme objects of particle astrophysics

    NASA Astrophysics Data System (ADS)

    Linsley, J.

    2001-08-01

    The name 'white hole' is intended to exploit a lively interest by the lay public in black holes, the extreme objects of gravitational astrophysics. Black holes are understood as openings into which entering matter disappears from observation. White holes are openings out of which matter emerges, often with properties that have been given intriguing names: 'strangeness', 'charm' and the like. These are properties that are unknowable by ordinary observations, but they are absolutely necessary for a deep understanding of the laws that govern the universe. White holes open up briefly in events where energy is made available for creating of particle-antiparticle pairs. The first observed events of this kind were encounters of cosmic rays with nuclei. A series of Nobel Prize-winning discoveries made by observing such events led to the development of giant particle accelerators for generating controlled beams of artificial cosmic rays. The name 'white hole' also fits decay events. White holes are ranked by the amount of energy that is made available. In case of ordinary cosmic ray collisions, this is about 1010 eV. A milestone in accelerator physics was the discovery of W and Z bosons. Decays of these exotic particles liberates more than 1011 eV. But the cosmic ray energy spectrum extends beyond 1020 eV. Where do such cosmic rays come from? Do they emerge from white holes that liberate even greater amounts of energy? The decay of X-particles with energy of order 1024 eV has been suggested. Why stop there? I'm intrigued by the notion of an 'ur-particle' so massive- energy equivalent 1090 eV-that its decay opened the ultimate white hole from which the universe emerged. This way of accounting for the 'big bang' implies that the fundamental laws already existed; they weren't violated at the first instant. One can imagine that the ur-particle decays into other extreme -mass particles in a chain that accounts for structure (clustering and voids) in an alternative manner than by

  6. Long Swarms and Short Swarms

    NASA Astrophysics Data System (ADS)

    McNutt, S. R.

    2011-12-01

    Many earthquake swarms at volcanoes last several months, then have a sharp uptick in rate in the hours before eruption. Examples include 2006 Augustine, 8.5 months then 10 hours; 1992 Spurr, 10 months then 4 hours; 1994 Rabaul, ~1 year then 27 hours; 2008 Kasatochi, 6 weeks then 2 days; and 2011 Puyuehue Cordon Caulle, 5 weeks then 2 days. For the well studied Augustine case, broadband data showed that very long period (VLP) energy accompanied 221 of 722 located earthquakes in the 10 hours before the first explosive eruption on 11 January 2006. This was revealed by low-pass filtering and the period of the VLP signal was 50 sec. The Augustine broadband stations were campaign instruments at distances of 2-3 km from the vent. No similar VLP energy has been found in events during the 8.5 month long swarm. Okmok volcano had a short swarm only lasting 5 hours prior to its 12 July 2008 eruption. Low-pass filtering of data from broadband station OKSO, 10 km from the vent, showed that 23 of 42 located events had VLP energy with a period of 30-40 sec. Events from Kasatochi volcano were scanned on station ATKA. Here the broadband station is much farther away at 88 km but the earthquakes in the short swarm 7 August 2008 were much larger with many M>3 events. The station suffered data gaps so only a few hours of data were scanned but numerous events were observed with VLP energy starting just after the P phase. Low-pass filtering showed VLP energy with a period of 10-12 sec. No VLP energy has been found in events of the preceding 6 week long swarm. These observations at three different volcanoes suggest that the short swarms represent a different process than the long swarms. The long swarms likely reflect pressure increases in the surrounding country rock caused by increasing magma pressure. The short swarms in contrast, appear to represent discrete pulses of magma injection at shallow depths. For all three volcanoes the earthquakes looked like typical volcano-tectonic (VT

  7. Complexity and Fly Swarms

    NASA Astrophysics Data System (ADS)

    Cates, Grant; Murray, Joelle

    Complexity is the study of phenomena that emerge from a collection of interacting objects and arises in many systems throughout physics, biology, finance, economics and more. Certain kinds of complex systems can be described by self-organized criticality (SOC). An SOC system is one that is internally driven towards some critical state. Recent experimental work suggests scaling behavior of fly swarms-one of the hallmarks of an SOC system. Our goal is to look for SOC behavior in computational models of fly swarms.

  8. Optimal design of an in-situ bioremediation system using support vector machine and particle swarm optimization

    NASA Astrophysics Data System (ADS)

    ch, Sudheer; Kumar, Deepak; Prasad, Ram Kailash; Mathur, Shashi

    2013-08-01

    A methodology based on support vector machine and particle swarm optimization techniques (SVM-PSO) was used in this study to determine an optimal pumping rate and well location to achieve an optimal cost of an in-situ bioremediation system. In the first stage of the two stage methodology suggested for optimal in-situ bioremediation design, the optimal number of wells and their locations was determined from preselected candidate well locations. The pumping rate and well location in the first stage were subsequently optimized in the second stage of the methodology. The highly nonlinear system of equations governing in-situ bioremediation comprises the equations of flow and solute transport coupled with relevant biodegradation kinetics. A finite difference model was developed to simulate the process of in-situ bioremediation using an Alternate-Direction Implicit technique. This developed model (BIOFDM) yields the spatial and temporal distribution of contaminant concentration for predefined initial and boundary conditions. BIOFDM was later validated by comparing the simulated results with those obtained using BIOPLUME III for the case study of Shieh and Peralta (2005). The results were found to be in close agreement. Moreover, since the solution of the highly nonlinear equation otherwise requires significant computational effort, the computational burden in this study was managed within a practical time frame by replacing the BIOFDM model with a trained SVM model. Support Vector Machine which generates fast solutions in real time was considered to be a universal function approximator in the study. Apart from reducing the computational burden, this technique generates a set of near optimal solutions (instead of a single optimal solution) and creates a re-usable data base that could be used to address many other management problems. Besides this, the search for an optimal pumping pattern was directed by a simple PSO technique and a penalty parameter approach was adopted

  9. Multi-Sensor Detection with Particle Swarm Optimization for Time-Frequency Coded Cooperative WSNs Based on MC-CDMA for Underground Coal Mines.

    PubMed

    Xu, Jingjing; Yang, Wei; Zhang, Linyuan; Han, Ruisong; Shao, Xiaotao

    2015-01-01

    In this paper, a wireless sensor network (WSN) technology adapted to underground channel conditions is developed, which has important theoretical and practical value for safety monitoring in underground coal mines. According to the characteristics that the space, time and frequency resources of underground tunnel are open, it is proposed to constitute wireless sensor nodes based on multicarrier code division multiple access (MC-CDMA) to make full use of these resources. To improve the wireless transmission performance of source sensor nodes, it is also proposed to utilize cooperative sensors with good channel conditions from the sink node to assist source sensors with poor channel conditions. Moreover, the total power of the source sensor and its cooperative sensors is allocated on the basis of their channel conditions to increase the energy efficiency of the WSN. To solve the problem that multiple access interference (MAI) arises when multiple source sensors transmit monitoring information simultaneously, a kind of multi-sensor detection (MSD) algorithm with particle swarm optimization (PSO), namely D-PSO, is proposed for the time-frequency coded cooperative MC-CDMA WSN. Simulation results show that the average bit error rate (BER) performance of the proposed WSN in an underground coal mine is improved significantly by using wireless sensor nodes based on MC-CDMA, adopting time-frequency coded cooperative transmission and D-PSO algorithm with particle swarm optimization. PMID:26343660

  10. Multi-Sensor Detection with Particle Swarm Optimization for Time-Frequency Coded Cooperative WSNs Based on MC-CDMA for Underground Coal Mines

    PubMed Central

    Xu, Jingjing; Yang, Wei; Zhang, Linyuan; Han, Ruisong; Shao, Xiaotao

    2015-01-01

    In this paper, a wireless sensor network (WSN) technology adapted to underground channel conditions is developed, which has important theoretical and practical value for safety monitoring in underground coal mines. According to the characteristics that the space, time and frequency resources of underground tunnel are open, it is proposed to constitute wireless sensor nodes based on multicarrier code division multiple access (MC-CDMA) to make full use of these resources. To improve the wireless transmission performance of source sensor nodes, it is also proposed to utilize cooperative sensors with good channel conditions from the sink node to assist source sensors with poor channel conditions. Moreover, the total power of the source sensor and its cooperative sensors is allocated on the basis of their channel conditions to increase the energy efficiency of the WSN. To solve the problem that multiple access interference (MAI) arises when multiple source sensors transmit monitoring information simultaneously, a kind of multi-sensor detection (MSD) algorithm with particle swarm optimization (PSO), namely D-PSO, is proposed for the time-frequency coded cooperative MC-CDMA WSN. Simulation results show that the average bit error rate (BER) performance of the proposed WSN in an underground coal mine is improved significantly by using wireless sensor nodes based on MC-CDMA, adopting time-frequency coded cooperative transmission and D-PSO algorithm with particle swarm optimization. PMID:26343660

  11. Time-delayed autosynchronous swarm control.

    PubMed

    Biggs, James D; Bennet, Derek J; Dadzie, S Kokou

    2012-01-01

    In this paper a general Morse potential model of self-propelling particles is considered in the presence of a time-delayed term and a spring potential. It is shown that the emergent swarm behavior is dependent on the delay term and weights of the time-delayed function, which can be set to induce a stationary swarm, a rotating swarm with uniform translation, and a rotating swarm with a stationary center of mass. An analysis of the mean field equations shows that without a spring potential the motion of the center of mass is determined explicitly by a multivalued function. For a nonzero spring potential the swarm converges to a vortex formation about a stationary center of mass, except at discrete bifurcation points where the center of mass will periodically trace an ellipse. The analytical results defining the behavior of the center of mass are shown to correspond with the numerical swarm simulations. PMID:22400623

  12. Time-delayed autosynchronous swarm control

    NASA Astrophysics Data System (ADS)

    Biggs, James D.; Bennet, Derek J.; Dadzie, S. Kokou

    2012-01-01

    In this paper a general Morse potential model of self-propelling particles is considered in the presence of a time-delayed term and a spring potential. It is shown that the emergent swarm behavior is dependent on the delay term and weights of the time-delayed function, which can be set to induce a stationary swarm, a rotating swarm with uniform translation, and a rotating swarm with a stationary center of mass. An analysis of the mean field equations shows that without a spring potential the motion of the center of mass is determined explicitly by a multivalued function. For a nonzero spring potential the swarm converges to a vortex formation about a stationary center of mass, except at discrete bifurcation points where the center of mass will periodically trace an ellipse. The analytical results defining the behavior of the center of mass are shown to correspond with the numerical swarm simulations.

  13. Capture of planetesimals into a circumterrestrial swarm

    SciTech Connect

    Weidenschilling, S.J.

    1984-01-01

    The lunar origin model considered involves processing of protolunar material through a circumterrestrial swarm of particles. Once such a swarm has formed, it can gain mass by capturing infalling planetesimals and ejecta from giant impacts on the Earth, although the angular momentum supply from these sources remains a problem. Examined is the first stage of formation of a geocentric swarm by capture of planetesimals from initialy heliocentric orbits. The only plausible capture mechanism that is not dependent on very low approach velocities is the mutual collision of planetesimals passing within Earth's sphere of influence. This capture scenario was tested directly by many body numerical integration of planetesimal orbits in near Earth space. Results agree that the systematic contribution of angular momentum is insufficient to maintain an orbiting swarm under heavy bombardment. Thus, a circumterrestrial swarm can be formed rather easily, but is hard to sustain because the mean net angular momentum of a many body swarm is small.

  14. Binary particle swarm optimized 2  ×  2 power splitters in a standard foundry silicon photonic platform.

    PubMed

    Mak, Jason C C; Sideris, Constantine; Jeong, Junho; Hajimiri, Ali; Poon, Joyce K S

    2016-08-15

    Compact power splitters designed ab initio using binary particle swarm optimization in a 2D mesh for a standard foundry silicon photonic platform are studied. Designs with a 4.8  μm×4.8  μm footprint composed of 200  nm×200  nm and 100  nm×100  nm cells are demonstrated. Despite not respecting design rules, the design with the smaller cells had lower insertion losses and broader bandwidth and showed consistent behavior across the wafer. Deviations between design and experiments point to the need for further investigations of the minimum feature dimensions. PMID:27519110

  15. Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm

    PubMed Central

    Wang, Jie-Sheng; Han, Shuang

    2015-01-01

    For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, a feed-forward neural network (FNN) based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA) is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:26583034

  16. Multiplex protein pattern unmixing using a non-linear variable-weighted support vector machine as optimized by a particle swarm optimization algorithm.

    PubMed

    Yang, Qin; Zou, Hong-Yan; Zhang, Yan; Tang, Li-Juan; Shen, Guo-Li; Jiang, Jian-Hui; Yu, Ru-Qin

    2016-01-15

    Most of the proteins locate more than one organelle in a cell. Unmixing the localization patterns of proteins is critical for understanding the protein functions and other vital cellular processes. Herein, non-linear machine learning technique is proposed for the first time upon protein pattern unmixing. Variable-weighted support vector machine (VW-SVM) is a demonstrated robust modeling technique with flexible and rational variable selection. As optimized by a global stochastic optimization technique, particle swarm optimization (PSO) algorithm, it makes VW-SVM to be an adaptive parameter-free method for automated unmixing of protein subcellular patterns. Results obtained by pattern unmixing of a set of fluorescence microscope images of cells indicate VW-SVM as optimized by PSO is able to extract useful pattern features by optimally rescaling each variable for non-linear SVM modeling, consequently leading to improved performances in multiplex protein pattern unmixing compared with conventional SVM and other exiting pattern unmixing methods. PMID:26592652

  17. Support vector machine based training of multilayer feedforward neural networks as optimized by particle swarm algorithm: application in QSAR studies of bioactivity of organic compounds.

    PubMed

    Lin, Wei-Qi; Jiang, Jian-Hui; Zhou, Yan-Ping; Wu, Hai-Long; Shen, Guo-Li; Yu, Ru-Qin

    2007-01-30

    Multilayer feedforward neural networks (MLFNNs) are important modeling techniques widely used in QSAR studies for their ability to represent nonlinear relationships between descriptors and activity. However, the problems of overfitting and premature convergence to local optima still pose great challenges in the practice of MLFNNs. To circumvent these problems, a support vector machine (SVM) based training algorithm for MLFNNs has been developed with the incorporation of particle swarm optimization (PSO). The introduction of the SVM based training mechanism imparts the developed algorithm with inherent capacity for combating the overfitting problem. Moreover, with the implementation of PSO for searching the optimal network weights, the SVM based learning algorithm shows relatively high efficiency in converging to the optima. The proposed algorithm has been evaluated using the Hansch data set. Application to QSAR studies of the activity of COX-2 inhibitors is also demonstrated. The results reveal that this technique provides superior performance to backpropagation (BP) and PSO training neural networks. PMID:17186488

  18. Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

    PubMed

    Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

    2015-10-01

    The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features. PMID:26289628

  19. Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region

    PubMed Central

    Yang, Qidong; Zuo, Hongchao; Li, Weidong

    2016-01-01

    Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large. PMID:26991786

  20. Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region.

    PubMed

    Yang, Qidong; Zuo, Hongchao; Li, Weidong

    2016-01-01

    Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large. PMID:26991786

  1. Modeling partially coupled objects with smooth particle hydrodynamics

    SciTech Connect

    Wingate, C.A.

    1996-10-01

    A very simple phenomenological model is presented to model objects that are partially coupled (i.e. welded or bonded) where usually the coupled interface is weaker than the bulk material. The model works by letting objects fully interact in compression and having the objects only partially interact in tension. A disconnect factor is provided to adjust the tensile interaction to simulate coupling strengths. Three cases of an example impact calculation are shown-no coupling, full coupling and partial coupling.

  2. Swarm Economics

    NASA Astrophysics Data System (ADS)

    Kazadi, Sanza; Lee, John

    The Hamiltonian Method of Swarm Design is applied to the design of an agent based economic system. The method allows the design of a system from the global behaviors to the agent behaviors, with a guarantee that once certain derived agent-level conditions are satisfied, the system behavior becomes the desired behavior. Conditions which must be satisfied by consumer agents in order to bring forth the `invisible hand of the market' are derived and demonstrated in simulation. A discussion of how this method might be extended to other economic systems and non-economic systems is presented.

  3. Evolution of photon and particle spectra in compact, luminous objects

    NASA Technical Reports Server (NTRS)

    Eilek, Jean A.; Caroff, Lawrence J.; Noerdlinger, Peter D.

    1988-01-01

    The physics of high energy photons and particles (especially electrons and positrons) in the compact, high-energy-density of galactic nuclei and quasars was investigated. A numerical code was developed which follows the nonlinear spectral evolution of a pair/photon plasma, due to two-body scattering and interaction process, in an unmagnetized system. The code was applied both to static plasmas and to relativistic expanding winds.

  4. Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids.

    PubMed

    Christobel, M; Tamil Selvi, S; Benedict, Shajulin

    2015-01-01

    One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO) algorithm based on the particle's best position (pbDPSO) and global best position (gbDPSO) is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER) and Dynamic Voltage Scaling (DVS) were used in the proposed DPSO algorithm. PMID:26075296

  5. Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids

    PubMed Central

    Christobel, M.; Tamil Selvi, S.; Benedict, Shajulin

    2015-01-01

    One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption. Proficient scheduling of Bag-of-Tasks in the grid environment ravages in minimum computation time. In this paper, a novel discrete particle swarm optimization (DPSO) algorithm based on the particle's best position (pbDPSO) and global best position (gbDPSO) is adopted to find the global optimal solution for higher dimensions. This novel DPSO yields better schedule with minimum computation time compared to Earliest Deadline First (EDF) and First Come First Serve (FCFS) algorithms which comparably reduces energy. Other scheduling parameters, such as job completion ratio and lateness, are also calculated and compared with EDF and FCFS. An energy improvement of up to 28% was obtained when Makespan Conservative Energy Reduction (MCER) and Dynamic Voltage Scaling (DVS) were used in the proposed DPSO algorithm. PMID:26075296

  6. Multi-scale analysis of collective behavior in 2D self-propelled particle models of swarms: An Advection-Diffusion with Memory Approach

    NASA Astrophysics Data System (ADS)

    Raghib, Michael; Levin, Simon; Kevrekidis, Ioannis

    2010-05-01

    Self-propelled particle models (SPP's) are a class of agent-based simulations that have been successfully used to explore questions related to various flavors of collective motion, including flocking, swarming, and milling. These models typically consist of particle configurations, where each particle moves with constant speed, but changes its orientation in response to local averages of the positions and orientations of its neighbors found within some interaction region. These local averages are based on `social interactions', which include avoidance of collisions, attraction, and polarization, that are designed to generate configurations that move as a single object. Errors made by the individuals in the estimates of the state of the local configuration are modeled as a random rotation of the updated orientation resulting from the social rules. More recently, SPP's have been introduced in the context of collective decision-making, where the main innovation consists of dividing the population into naïve and `informed' individuals. Whereas naïve individuals follow the classical collective motion rules, members of the informed sub-population update their orientations according to a weighted average of the social rules and a fixed `preferred' direction, shared by all the informed individuals. Collective decision-making is then understood in terms of the ability of the informed sub-population to steer the whole group along the preferred direction. Summary statistics of collective decision-making are defined in terms of the stochastic properties of the random walk followed by the centroid of the configuration as the particles move about, in particular the scaling behavior of the mean squared displacement (msd). For the region of parameters where the group remains coherent , we note that there are two characteristic time scales, first there is an anomalous transient shared by both purely naïve and informed configurations, i.e. the scaling exponent lies between 1 and

  7. Swarm Verification

    NASA Technical Reports Server (NTRS)

    Holzmann, Gerard J.; Joshi, Rajeev; Groce, Alex

    2008-01-01

    Reportedly, supercomputer designer Seymour Cray once said that he would sooner use two strong oxen to plow a field than a thousand chickens. Although this is undoubtedly wise when it comes to plowing a field, it is not so clear for other types of tasks. Model checking problems are of the proverbial "search the needle in a haystack" type. Such problems can often be parallelized easily. Alas, none of the usual divide and conquer methods can be used to parallelize the working of a model checker. Given that it has become easier than ever to gain access to large numbers of computers to perform even routine tasks it is becoming more and more attractive to find alternate ways to use these resources to speed up model checking tasks. This paper describes one such method, called swarm verification.

  8. Null steering of adaptive beamforming using linear constraint minimum variance assisted by particle swarm optimization, dynamic mutated artificial immune system, and gravitational search algorithm.

    PubMed

    Darzi, Soodabeh; Kiong, Tiong Sieh; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Salem, Balasem

    2014-01-01

    Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program. PMID:25147859

  9. Displacement back analysis for a high slope of the Dagangshan Hydroelectric Power Station based on BP neural network and particle swarm optimization.

    PubMed

    Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

    2014-01-01

    The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes. PMID:25140345

  10. Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation.

    PubMed

    Mekhmoukh, Abdenour; Mokrani, Karim

    2015-11-01

    In this paper, a new image segmentation method based on Particle Swarm Optimization (PSO) and outlier rejection combined with level set is proposed. A traditional approach to the segmentation of Magnetic Resonance (MR) images is the Fuzzy C-Means (FCM) clustering algorithm. The membership function of this conventional algorithm is sensitive to the outlier and does not integrate the spatial information in the image. The algorithm is very sensitive to noise and in-homogeneities in the image, moreover, it depends on cluster centers initialization. To improve the outlier rejection and to reduce the noise sensitivity of conventional FCM clustering algorithm, a novel extended FCM algorithm for image segmentation is presented. In general, in the FCM algorithm the initial cluster centers are chosen randomly, with the help of PSO algorithm the clusters centers are chosen optimally. Our algorithm takes also into consideration the spatial neighborhood information. These a priori are used in the cost function to be optimized. For MR images, the resulting fuzzy clustering is used to set the initial level set contour. The results confirm the effectiveness of the proposed algorithm. PMID:26299609

  11. Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization

    PubMed Central

    Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

    2014-01-01

    The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes. PMID:25140345

  12. Underwater gas pipeline leakage source localization by distributed fiber-optic sensing based on particle swarm optimization tuning of the support vector machine.

    PubMed

    Huang, Yue; Wang, Qiang; Shi, Lilian; Yang, Qihua

    2016-01-10

    Accurate underwater gas pipeline leak localization requires particular attention due to the sensitivity of environmental conditions. Experiments were performed to analyze the localization performance of a distributed optical fiber sensing system based on the hybrid Sagnac and Mach-Zehnder interferometer. The traditional null frequency location method does not easily allow accurate location of the leakage points. To improve the positioning accuracy, the particle swarm optimization algorithm (PSO) tuning of the support vector machine (SVM) was used to predict the leakage points based on gathered leakage data. The PSO is able to optimize the SVM parameters. For the 10 km range chosen, the results show the PSO-SVM average absolute error of the leakage points predicted is 66 m. The prediction accuracy of leakage points is 98.25% by PSO tuning of the SVM processing. For 20 leakage test data points, the average absolute error of leakage point location is 124.8 m. The leakage position predicted by the PSO algorithm after optimization of the parameters is more accurate. PMID:26835758

  13. Joint inversion of T1-T2 spectrum combining the iterative truncated singular value decomposition and the parallel particle swarm optimization algorithms

    NASA Astrophysics Data System (ADS)

    Ge, Xinmin; Wang, Hua; Fan, Yiren; Cao, Yingchang; Chen, Hua; Huang, Rui

    2016-01-01

    With more information than the conventional one dimensional (1D) longitudinal relaxation time (T1) and transversal relaxation time (T2) spectrums, a two dimensional (2D) T1-T2 spectrum in a low field nuclear magnetic resonance (NMR) is developed to discriminate the relaxation components of fluids such as water, oil and gas in porous rock. However, the accuracy and efficiency of the T1-T2 spectrum are limited by the existing inversion algorithms and data acquisition schemes. We introduce a joint method to inverse the T1-T2 spectrum, which combines iterative truncated singular value decomposition (TSVD) and a parallel particle swarm optimization (PSO) algorithm to get fast computational speed and stable solutions. We reorganize the first kind Fredholm integral equation of two kernels to a nonlinear optimization problem with non-negative constraints, and then solve the ill-conditioned problem by the iterative TSVD. Truncating positions of the two diagonal matrices are obtained by the Akaike information criterion (AIC). With the initial values obtained by TSVD, we use a PSO with parallel structure to get the global optimal solutions with a high computational speed. We use the synthetic data with different signal to noise ratio (SNR) to test the performance of the proposed method. The result shows that the new inversion algorithm can achieve favorable solutions for signals with SNR larger than 10, and the inversion precision increases with the decrease of the components of the porous rock.

  14. Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

    PubMed Central

    Sieh Kiong, Tiong; Tariqul Islam, Mohammad; Ismail, Mahamod; Salem, Balasem

    2014-01-01

    Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program. PMID:25147859

  15. Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm

    NASA Astrophysics Data System (ADS)

    Chen, Xia; Hu, Hong-li; Liu, Fei; Gao, Xiang Xiang

    2011-10-01

    The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitances between sets of electrodes placed around its periphery. In view of the nonlinear relationship between the permittivity distribution and capacitances and the limited number of independent capacitance measurements, image reconstruction for ECT is a nonlinear and ill-posed inverse problem. To solve this problem, a new image reconstruction method for ECT based on a least-squares support vector machine (LS-SVM) combined with a self-adaptive particle swarm optimization (PSO) algorithm is presented. Regarded as a special small sample theory, the SVM avoids the issues appearing in artificial neural network methods such as difficult determination of a network structure, over-learning and under-learning. However, the SVM performs differently with different parameters. As a relatively new population-based evolutionary optimization technique, PSO is adopted to realize parameters' effective selection with the advantages of global optimization and rapid convergence. This paper builds up a 12-electrode ECT system and a pneumatic conveying platform to verify this image reconstruction algorithm. Experimental results indicate that the algorithm has good generalization ability and high-image reconstruction quality.

  16. [Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

    PubMed

    Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

    2015-12-01

    A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus. PMID:27169290

  17. Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

    PubMed Central

    Alwee, Razana; Hj Shamsuddin, Siti Mariyam; Sallehuddin, Roselina

    2013-01-01

    Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models. PMID:23766729

  18. Collective motion with anticipation: flocking, spinning, and swarming.

    PubMed

    Morin, Alexandre; Caussin, Jean-Baptiste; Eloy, Christophe; Bartolo, Denis

    2015-01-01

    We investigate the collective dynamics of self-propelled particles able to probe and anticipate the orientation of their neighbors. We show that a simple anticipation strategy hinders the emergence of homogeneous flocking patterns. Yet anticipation promotes two other forms of self-organization: collective spinning and swarming. In the spinning phase, all particles follow synchronous circular orbits, while in the swarming phase, the population condensates into a single compact swarm that cruises coherently without requiring any cohesive interactions. We quantitatively characterize and rationalize these phases of polar active matter and discuss potential applications to the design of swarming robots. PMID:25679597

  19. Collective motion with anticipation: Flocking, spinning, and swarming

    NASA Astrophysics Data System (ADS)

    Morin, Alexandre; Caussin, Jean-Baptiste; Eloy, Christophe; Bartolo, Denis

    2015-01-01

    We investigate the collective dynamics of self-propelled particles able to probe and anticipate the orientation of their neighbors. We show that a simple anticipation strategy hinders the emergence of homogeneous flocking patterns. Yet anticipation promotes two other forms of self-organization: collective spinning and swarming. In the spinning phase, all particles follow synchronous circular orbits, while in the swarming phase, the population condensates into a single compact swarm that cruises coherently without requiring any cohesive interactions. We quantitatively characterize and rationalize these phases of polar active matter and discuss potential applications to the design of swarming robots.

  20. Effects of exposure to heavy particles and aging on object recognition memory in rats

    NASA Astrophysics Data System (ADS)

    Rabin, Bernard; Joseph, James; Shukitt-Hale, Barbara; Carrihill-Knoll, Kirsty; Shannahan, Ryan; Hering, Kathleen

    Exposure to HZE particles produces changes in neurocognitive performance. These changes, including deficits in spatial learning and memory, object recognition memory and operant responding, are also observed in the aged organism. As such, it has been proposed that exposure to heavy particles produces "accelerated aging". Because aging is an ongoing process, it is possible that there would be an interaction between the effects of exposure and the effects of aging, such that doses of HZE particles that do not affect the performance of younger organisms will affect the performance of organisms as they age. The present experiments were designed to test the hypothesis that young rats that had been exposed to HZE particles would show a progressive deterioration in object recognition memory as a function of the age of testing. Rats were exposed to 12 C, 28 S or 48 Ti particles at the N.A.S.A. Space Radiation Laboratory at Brookhaven National Laboratory. Following irradiation the rats were shipped to UMBC for behavioral testing. HZE particle-induced changes in object recognition memory were tested using a standard procedure: rats were placed in an open field and allowed to interact with two identical objects for up to 30 sec; twenty-four hrs later the rats were again placed in the open field, this time containing one familiar and one novel object. Non-irradiated control animals spent significantly more time with the novel object than with the familiar object. In contrast, the rats that been exposed to heavy particles spent equal amounts of time with both the novel and familiar object. The lowest dose of HZE particles which produced a disruption of object recognition memory was determined three months and eleven months following exposure. The threshold dose needed to disrupt object recognition memory three months following irradiation varied as a function of the specific particle and energy. When tested eleven months following irradiation, doses of HZE particles that did

  1. Object detection and tracking with active camera on motion vectors of feature points and particle filter.

    PubMed

    Chen, Yong; Zhang, Rong-Hua; Shang, Lei; Hu, Eric

    2013-06-01

    A method based on motion vectors of feature points and particle filter has been proposed and developed for an active∕moving camera for object detection and tracking purposes. The object is detected by histogram of motion vectors first, and then, on the basis of particle filter algorithm, the weighing factors are obtained via color information. In addition, re-sampling strategy and surf feature points are used to remedy the drawback of particle degeneration. Experimental results demonstrate the practicability and accuracy of the new method and are presented in the paper. PMID:23822380

  2. Earthquake swarms in Greenland

    NASA Astrophysics Data System (ADS)

    Larsen, Tine B.; Voss, Peter H.; Dahl-Jensen, Trine

    2014-05-01

    Earthquake swarms occur primarily near active volcanoes and in areas with frequent tectonic activity. However, intraplate earthquake swarms are not an unknown phenomenon. They are located near zones of weakness, e.g. in regions with geological contrasts, where dynamic processes are active. An earthquake swarm is defined as a period of increased seismicity, in the form of a cluster of earthquakes of similar magnitude, occurring in the same general area, during a limited time period. There is no obvious main shock among the earthquakes in a swarm. Earthquake swarms occur in Greenland, which is a tectonically stable, intraplate environment. The first earthquake swarms in Greenland were detected more than 30 years ago in Northern and North-Eastern Greenland. However, detection of these low-magnitude events is challenging due to the enormous distances and the relatively sparse network of seismographs. The seismograph coverage of Greenland has vastly improved since the international GLISN-project was initiated in 2008. Greenland is currently coved by an open network of 19 BB seismographs, most of them transmitting data in real-time. Additionally, earthquake activity in Greenland is monitored by seismographs in Canada, Iceland, on Jan Mayen, and on Svalbard. The time-series of data from the GLISN network is still short, with the latest station been added in NW Greenland in 2013. However, the network has already proven useful in detecting several earthquake swarms. In this study we will focus on two swarms: one occurring near/on the East Greenland coast in 2008, and another swarm occurring in the Disko-area near the west coast of Greenland in 2010. Both swarms consist of earthquakes with local magnitudes between 1.9 and 3.2. The areas, where the swarms are located, are regularly active with small earthquakes. The earthquake swarms are analyzed in the context of the general seismicity and the possible relationship to the local geological conditions.

  3. Swarms of UAVs and fighter aircraft

    SciTech Connect

    Trahan, M.W.; Wagner, J.S.; Stantz, K.M.; Gray, P.C.; Robinett, R.

    1998-11-01

    This paper describes a method of modeling swarms of UAVs and/or fighter aircraft using particle simulation concepts. Recent investigations into the use of genetic algorithms to design neural networks for the control of autonomous vehicles (i.e., robots) led to the examination of methods of simulating large collections of robots. This paper describes the successful implementation of a model of swarm dynamics using particle simulation concepts. Several examples of the complex behaviors achieved in a target/interceptor scenario are presented.

  4. Solving Fractional Programming Problems based on Swarm Intelligence

    NASA Astrophysics Data System (ADS)

    Raouf, Osama Abdel; Hezam, Ibrahim M.

    2014-04-01

    This paper presents a new approach to solve Fractional Programming Problems (FPPs) based on two different Swarm Intelligence (SI) algorithms. The two algorithms are: Particle Swarm Optimization, and Firefly Algorithm. The two algorithms are tested using several FPP benchmark examples and two selected industrial applications. The test aims to prove the capability of the SI algorithms to solve any type of FPPs. The solution results employing the SI algorithms are compared with a number of exact and metaheuristic solution methods used for handling FPPs. Swarm Intelligence can be denoted as an effective technique for solving linear or nonlinear, non-differentiable fractional objective functions. Problems with an optimal solution at a finite point and an unbounded constraint set, can be solved using the proposed approach. Numerical examples are given to show the feasibility, effectiveness, and robustness of the proposed algorithm. The results obtained using the two SI algorithms revealed the superiority of the proposed technique among others in computational time. A better accuracy was remarkably observed in the solution results of the industrial application problems.

  5. Swarm: ESA's Magnetic Field Mission

    NASA Astrophysics Data System (ADS)

    Haagmans, R.; Menard, Y.; Floberghagen, R.; Plank, G.; Drinkwater, M. R.

    2010-12-01

    Swarm is the fifth Earth Explorer mission in ESA’s Living Planet Programme. The objective of the Swarm mission is to provide the best ever survey of the geomagnetic field and its temporal evolution. The Mission shall deliver data that allow access to new insights into the Earth system by improving our understanding of the Earth’s interior and near-Earth electro-magnetic environment. After release from a single launcher, a side-by-side flying slowly decaying lower pair of satellites will be released at an initial altitude of about 490 km together with a third satellite that will be lifted to 530 km to complete the Swarm constellation. High-precision and high-resolution measurements of the strength, direction and variation of the magnetic field, complemented by precise navigation, accelerometer and electric field measurements, will provide the observations that are required to separate and model various sources of the geomagnetic field and near-Earth current systems. The mission aims to provide a unique view into Earth core dynamics, mantle conductivity, crustal magnetisation, ionospheric and magnetospheric current systems and upper atmosphere dynamics - ranging from understanding the geodynamo to contributing to space weather. The scientific objectives and results from recent scientific studies will be presented. In addition the current status of the project, which is presently in the development phase, will be addressed. The mission is scheduled for launch in 2012.

  6. The Fate of Colloidal Swarms in Fractures

    NASA Astrophysics Data System (ADS)

    Pyrak-Nolte, L. J.; Olander, M. K.

    2009-12-01

    In the next 10-20 years, nano- and micro-sensor engineering will advance to the stage where sensor swarms could be deployed in the subsurface to probe rock formations and the fluids contained in them. Sensor swarms are groups of nano- or micro- sensors that are maintained as a coherent group to enable either sensor-to-sensor communication and/or coherent transmission of information as a group. The ability to maintain a swarm of sensors depends on the complexity of the flow paths in the rock, on the size and shape of the sensors and on the chemical interaction among the sensors, fluids, and rock surfaces. In this study, we investigate the effect of fracture aperture and fluid currents on the formation, evolution and break-up of colloidal swarms under gravity. Transparent cubic samples (100 mm x 100 mm x 100 mm) containing synthetic fractures with uniform and non-uniform aperture distributions were used to quantify the effect of aperture on swarm formation, swarm velocity, and swarm geometry using optical imaging. A fracture with a uniform aperture distribution was fabricated from two polished rectangular prisms of acrylic. A fracture with a non-uniform aperture distribution was created with a polished rectangular acrylic prism and an acrylic replica of an induced fracture surface from a carbonate rock. A series of experiments were performed to determine how swarm movement and geometry are affected as the walls of the fracture are brought closer together from 50 mm to 1 mm. During the experiments, the fracture was fully saturated with water. We created the swarms using two different particle sizes in dilute suspension (~ 1.0% by mass) . The particles were 3 micron diameter fluorescent polymer beads and 25 micron diameter soda-lime glass beads. The swarm behavior was imaged using an optical fluorescent imaging system composed of a CCD camera illuminated by a 100 mW diode-pumped doubled YAG laser. A swam was created when approximately 0.01 g drop of the suspension was

  7. Hardware Accelerator for Run-Time Learning Adopted in Object Recognition with Cascade Particle Filter

    NASA Astrophysics Data System (ADS)

    Sugano, Hiroki; Ochi, Hiroyuki; Nakamura, Yukihiro; Miyamoto, Ryusuke

    Recently, many researchers tackle accurate object recognition algorithms and many algorithms are proposed. However, these algorithms have some problems caused by variety of real environments such as a direction change of the object or its shading change. The new tracking algorithm, Cascade Particle Filter, is proposed to fill such demands in real environments by constructing the object model while tracking the objects. We have been investigating to implement accurate object recognition on embedded systems in real-time. In order to apply the Cascade Particle Filter to embedded applications such as surveillance, automotives, and robotics, a hardware accelerator is indispensable because of limitations in power consumption. In this paper we propose a hardware implementation of the Discrete AdaBoost algorithm that is the most computationally intensive part of the Cascade Particle Filter. To implement the proposed hardware, we use PICO Express, a high level synthesis tool provided by Synfora, for rapid prototyping. Implementation result shows that the synthesized hardware has 1, 132, 038 transistors and the die area is 2,195µm × 1,985µm under a 0.180µm library. The simulation result shows that total processing time is about 8.2 milliseconds at 65MHz operation frequency.

  8. ESA Swarm Mission - Level 1b Products

    NASA Astrophysics Data System (ADS)

    Tøffner-Clausen, Lars; Floberghagen, Rune; Mecozzi, Riccardo; Menard, Yvon

    2014-05-01

    Swarm, a three-satellite constellation to study the dynamics of the Earth's magnetic field and its interactions with the Earth system, has been launched in November 2013. The objective of the Swarm mission is to provide the best ever survey of the geomagnetic field and its temporal evolution, which will bring new insights into the Earth system by improving our understanding of the Earth's interior and environment. The Level 1b Products of the Swarm mission contain time-series of the quality screened, calibrated, corrected, and fully geo-localized measurements of the magnetic field intensity, the magnetic field vector (provided in both instrument and Earth-fixed frames), the plasma density, temperature, and velocity. Additionally, quality screened and pre-calibrated measurements of the nongravitational accelerations are provided. Geo-localization is performed by 24- channel GPS receivers and by means of unique, three head Advanced Stellar Compasses for high-precision satellite attitude information. The Swarm Level 1b data will be provided in daily products separately for each of the three Swarm spacecrafts. This poster will present detailed lists of the contents of the Swarm Level 1b Products and brief descriptions of the processing algorithms used in the generation of these data.

  9. Collective navigation of cargo-carrying swarms

    PubMed Central

    Shklarsh, Adi; Finkelshtein, Alin; Ariel, Gil; Kalisman, Oren; Ingham, Colin; Ben-Jacob, Eshel

    2012-01-01

    Much effort has been devoted to the study of swarming and collective navigation of micro-organisms, insects, fish, birds and other organisms, as well as multi-agent simulations and to the study of real robots. It is well known that insect swarms can carry cargo. The studies here are motivated by a less well-known phenomenon: cargo transport by bacteria swarms. We begin with a concise review of how bacteria swarms carry natural, micrometre-scale objects larger than the bacteria (e.g. fungal spores) as well as man-made beads and capsules (for drug delivery). A comparison of the trajectories of virtual beads in simulations (using different putative coupling between the virtual beads and the bacteria) with the observed trajectories of transported fungal spores implies the existence of adaptable coupling. Motivated by these observations, we devised new, multi-agent-based studies of cargo transport by agent swarms. As a first step, we extended previous modelling of collective navigation of simple bacteria-inspired agents in complex terrain, using three putative models of agent–cargo coupling. We found that cargo-carrying swarms can navigate efficiently in a complex landscape. We further investigated how the stability, elasticity and other features of agent–cargo bonds influence the collective motion and the transport of the cargo, and found sharp phase shifts and dual successful strategies for cargo delivery. Further understanding of such mechanisms may provide valuable clues to understand cargo-transport by smart swarms of other organisms as well as by man-made swarming robots. PMID:24312731

  10. Object tracking with adaptive HOG detector and adaptive Rao-Blackwellised particle filter

    NASA Astrophysics Data System (ADS)

    Rosa, Stefano; Paleari, Marco; Ariano, Paolo; Bona, Basilio

    2012-01-01

    Scenarios for a manned mission to the Moon or Mars call for astronaut teams to be accompanied by semiautonomous robots. A prerequisite for human-robot interaction is the capability of successfully tracking humans and objects in the environment. In this paper we present a system for real-time visual object tracking in 2D images for mobile robotic systems. The proposed algorithm is able to specialize to individual objects and to adapt to substantial changes in illumination and object appearance during tracking. The algorithm is composed by two main blocks: a detector based on Histogram of Oriented Gradient (HOG) descriptors and linear Support Vector Machines (SVM), and a tracker which is implemented by an adaptive Rao-Blackwellised particle filter (RBPF). The SVM is re-trained online on new samples taken from previous predicted positions. We use the effective sample size to decide when the classifier needs to be re-trained. Position hypotheses for the tracked object are the result of a clustering procedure applied on the set of particles. The algorithm has been tested on challenging video sequences presenting strong changes in object appearance, illumination, and occlusion. Experimental tests show that the presented method is able to achieve near real-time performances with a precision of about 7 pixels on standard video sequences of dimensions 320 × 240.

  11. Guidance and control of swarms of spacecraft

    NASA Astrophysics Data System (ADS)

    Morgan, Daniel James

    There has been considerable interest in formation flying spacecraft due to their potential to perform certain tasks at a cheaper cost than monolithic spacecraft. Formation flying enables the use of smaller, cheaper spacecraft that distribute the risk of the mission. Recently, the ideas of formation flying have been extended to spacecraft swarms made up of hundreds to thousands of 100-gram-class spacecraft known as femtosatellites. The large number of spacecraft and limited capabilities of each individual spacecraft present a significant challenge in guidance, navigation, and control. This dissertation deals with the guidance and control algorithms required to enable the flight of spacecraft swarms. The algorithms developed in this dissertation are focused on achieving two main goals: swarm keeping and swarm reconfiguration. The objectives of swarm keeping are to maintain bounded relative distances between spacecraft, prevent collisions between spacecraft, and minimize the propellant used by each spacecraft. Swarm reconfiguration requires the transfer of the swarm to a specific shape. Like with swarm keeping, minimizing the propellant used and preventing collisions are the main objectives. Additionally, the algorithms required for swarm keeping and swarm reconfiguration should be decentralized with respect to communication and computation so that they can be implemented on femtosats, which have limited hardware capabilities. The algorithms developed in this dissertation are concerned with swarms located in low Earth orbit. In these orbits, Earth oblateness and atmospheric drag have a significant effect on the relative motion of the swarm. The complicated dynamic environment of low Earth orbits further complicates the swarm-keeping and swarm-reconfiguration problems. To better develop and test these algorithms, a nonlinear, relative dynamic model with J2 and drag perturbations is developed. This model is used throughout this dissertation to validate the algorithms

  12. From hybrid swarms to swarms of hybrids

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The introgression of modern humans (Homo sapiens) with Neanderthals 40,000 YBP after a half-million years of separation, may have led to the best example of a hybrid swarm on earth. Modern trade and transportation in support of the human hybrids has continued to introduce additional species, genotyp...

  13. Micro-object motion tracking based on the probability hypothesis density particle tracker.

    PubMed

    Shi, Chunmei; Zhao, Lingling; Wang, Junjie; Zhang, Chiping; Su, Xiaohong; Ma, Peijun

    2016-04-01

    Tracking micro-objects in the noisy microscopy image sequences is important for the analysis of dynamic processes in biological objects. In this paper, an automated tracking framework is proposed to extract the trajectories of micro-objects. This framework uses a probability hypothesis density particle filtering (PF-PHD) tracker to implement a recursive state estimation and trajectories association. In order to increase the efficiency of this approach, an elliptical target model is presented to describe the micro-objects using shape parameters instead of point-like targets which may cause inaccurate tracking. A novel likelihood function, not only covering the spatiotemporal distance but also dealing with geometric shape function based on the Mahalanobis norm, is proposed to improve the accuracy of particle weight in the update process of the PF-PHD tracker. Using this framework, a larger number of tracks are obtained. The experiments are performed on simulated data of microtubule movements and real mouse stem cells. We compare the PF-PHD tracker with the nearest neighbor method and the multiple hypothesis tracking method. Our PF-PHD tracker can simultaneously track hundreds of micro-objects in the microscopy image sequence. PMID:26084407

  14. A heuristic approach to the quantum measurement problem: How to distinguish particle detectors from ordinary objects

    NASA Astrophysics Data System (ADS)

    Merlin, R.

    2015-08-01

    Elementary particle detectors fall broadly into only two classes: phase-transformation devices, such as the bubble chamber, and charge-transfer devices like the Geiger-Müller tube. Quantum measurements are seen to involve transitions from a long-lived metastable state (e.g., superheated liquid or a gas of atoms between charged capacitor plates) to a thermodynamically stable condition. A detector is then a specially prepared object undergoing a metastable-to-stable transformation that is significantly enhanced by the presence of the measured particle, which behaves, in some sense, as the seed of a process of heterogeneous nucleation. Based on this understanding of the operation of a conventional detector, and using results of orthogonality-catastrophe theory, we argue that, in the thermodynamic limit, the pre-measurement Hamiltonian is not the same as that describing the detector during or after the interaction with a particle and, thus, that superpositions of pointer states (Schrödinger’s cats) are unphysical because their time evolution is ill defined. Examples of particle-induced changes in the Hamiltonian are also given for ordinary systems whose macroscopic parameters are susceptible to radiation damage, but are not modified by the interaction with a single particle.

  15. Effects of heavy particle irradiation and diet on object recognition memory in rats

    NASA Astrophysics Data System (ADS)

    Rabin, Bernard M.; Carrihill-Knoll, Kirsty; Hinchman, Marie; Shukitt-Hale, Barbara; Joseph, James A.; Foster, Brian C.

    2009-04-01

    On long-duration missions to other planets astronauts will be exposed to types and doses of radiation that are not experienced in low earth orbit. Previous research using a ground-based model for exposure to cosmic rays has shown that exposure to heavy particles, such as 56Fe, disrupts spatial learning and memory measured using the Morris water maze. Maintaining rats on diets containing antioxidant phytochemicals for 2 weeks prior to irradiation ameliorated this deficit. The present experiments were designed to determine: (1) the generality of the particle-induced disruption of memory by examining the effects of exposure to 56Fe particles on object recognition memory; and (2) whether maintaining rats on these antioxidant diets for 2 weeks prior to irradiation would also ameliorate any potential deficit. The results showed that exposure to low doses of 56Fe particles does disrupt recognition memory and that maintaining rats on antioxidant diets containing blueberry and strawberry extract for only 2 weeks was effective in ameliorating the disruptive effects of irradiation. The results are discussed in terms of the mechanisms by which exposure to these particles may produce effects on neurocognitive performance.

  16. Shelter in a Swarm.

    PubMed

    Harshey, Rasika M; Partridge, Jonathan D

    2015-11-20

    Flagella propel bacteria during both swimming and swarming, dispersing them widely. However, while swimming bacteria use chemotaxis to find nutrients and avoid toxic environments, swarming bacteria appear to suppress chemotaxis and to use the dynamics of their collective motion to continuously expand and acquire new territory, barrel through lethal chemicals in their path, carry along bacterial and fungal cargo that assists in exploration of new niches, and engage in group warfare for niche dominance. Here, we focus on two aspects of swarming, which, if understood, hold the promise of revealing new insights into microbial signaling and behavior, with ramifications beyond bacterial swarming. These are as follows: how bacteria sense they are on a surface and turn on programs that promote movement and how they override scarcity and adversity as dense packs. PMID:26277623

  17. Video object tracking using improved chamfer matching and condensation particle filter

    NASA Astrophysics Data System (ADS)

    Wu, Tao; Ding, Xiaoqing; Wang, Shengjin; Wang, Kongqiao

    2008-02-01

    Object tracking is an essential problem in the field of video and image processing. Although tracking algorithms working on gray video are convenient in actual applications, they are more difficult to be developed than those using color features, since less information is taken into account. Few researches have been dedicated to tracking object using edge information. In this paper, we proposed a novel video tracking algorithm based on edge information for gray videos. This method adopts the combination of a condensation particle filter and an improved chamfer matching. The improved chamfer matching is rotation invariant and capable of estimating the shift between an observed image patch and a template by an orientation distance transform. A modified discriminative likelihood measurement method that focuses on the difference is adopted. These values are normalized and used as the weights of particles which predict and track the object. Experiment results show that our modifications to chamfer matching improve its performance in video tracking problem. And the algorithm is stable, robust, and can effectively handle rotation distortion. Further work can be done on updating the template to adapt to significant viewpoint and scale changes of the appearance of the object during the tracking process.

  18. Swarms: Optimum aggregations of spacecraft

    NASA Technical Reports Server (NTRS)

    Mayer, H. L.

    1980-01-01

    Swarms are aggregations of spacecraft or elements of a space system which are cooperative in function, but physically isolated or only loosely connected. For some missions the swarm configuration may be optimum compared to a group of completely independent spacecraft or a complex rigidly integrated spacecraft or space platform. General features of swarms are induced by considering an ensemble of 26 swarms, examples ranging from Earth centered swarms for commercial application to swarms for exploring minor planets. A concept for a low altitude swarm as a substitute for a space platform is proposed and a preliminary design studied. The salient design feature is the web of tethers holding the 30 km swarm in a rigid two dimensional array in the orbital plane. A mathematical discussion and tutorial in tether technology and in some aspects of the distribution of services (mass, energy, and information to swarm elements) are included.

  19. Object-Oriented Parallel Particle-in-Cell Code for Beam Dynamics Simulation in Linear Accelerators

    SciTech Connect

    Qiang, J.; Ryne, R.D.; Habib, S.; Decky, V.

    1999-11-13

    In this paper, we present an object-oriented three-dimensional parallel particle-in-cell code for beam dynamics simulation in linear accelerators. A two-dimensional parallel domain decomposition approach is employed within a message passing programming paradigm along with a dynamic load balancing. Implementing object-oriented software design provides the code with better maintainability, reusability, and extensibility compared with conventional structure based code. This also helps to encapsulate the details of communications syntax. Performance tests on SGI/Cray T3E-900 and SGI Origin 2000 machines show good scalability of the object-oriented code. Some important features of this code also include employing symplectic integration with linear maps of external focusing elements and using z as the independent variable, typical in accelerators. A successful application was done to simulate beam transport through three superconducting sections in the APT linac design.

  20. Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

    PubMed

    Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

    2015-05-01

    A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions. PMID:25481821

  1. Dynamics of Bacterial Swarming

    PubMed Central

    Darnton, Nicholas C.; Turner, Linda; Rojevsky, Svetlana; Berg, Howard C.

    2010-01-01

    Abstract When vegetative bacteria that can swim are grown in a rich medium on an agar surface, they become multinucleate, elongate, synthesize large numbers of flagella, produce wetting agents, and move across the surface in coordinated packs: they swarm. We examined the motion of swarming Escherichia coli, comparing the motion of individual cells to their motion during swimming. Swarming cells' speeds are comparable to bulk swimming speeds, but very broadly distributed. Their speeds and orientations are correlated over a short distance (several cell lengths), but this correlation is not isotropic. We observe the swirling that is conspicuous in many swarming systems, probably due to increasingly long-lived correlations among cells that associate into groups. The normal run-tumble behavior seen in swimming chemotaxis is largely suppressed, instead, cells are continually reoriented by random jostling by their neighbors, randomizing their directions in a few tenths of a second. At the edge of the swarm, cells often pause, then swim back toward the center of the swarm or along its edge. Local alignment among cells, a necessary condition of many flocking theories, is accomplished by cell body collisions and/or short-range hydrodynamic interactions. PMID:20483315

  2. Identifying and quantifying interactions in a laboratory swarm

    NASA Astrophysics Data System (ADS)

    Puckett, James; Kelley, Douglas; Ouellette, Nicholas

    2013-03-01

    Emergent collective behavior, such as in flocks of birds or swarms of bees, is exhibited throughout the animal kingdom. Many models have been developed to describe swarming and flocking behavior using systems of self-propelled particles obeying simple rules or interacting via various potentials. However, due to experimental difficulties and constraints, little empirical data exists for characterizing the exact form of the biological interactions. We study laboratory swarms of flying Chironomus riparius midges, using stereoimaging and particle tracking techniques to record three-dimensional trajectories for all the individuals in the swarm. We describe methods to identify and quantify interactions by examining these trajectories, and report results on interaction magnitude, frequency, and mutuality.

  3. Turbulence of swarming sperm.

    PubMed

    Creppy, Adama; Praud, Olivier; Druart, Xavier; Kohnke, Philippa L; Plouraboué, Franck

    2015-09-01

    Collective motion of self-sustained swarming flows has recently provided examples of small-scale turbulence arising where viscous effects are dominant. We report the first observation of universal enstrophy cascade in concentrated swarming sperm consistent with a body of evidence built from various independent measurements. We found a well-defined k^{-3} power-law decay of a velocity field power spectrum and relative dispersion of small beads consistent with theoretical predictions in 2D turbulence. Concentrated living sperm displays long-range, correlated whirlpool structures of a size that provides an integral scale of turbulence. We propose a consistent explanation for this quasi-2D turbulence based on self-structured laminated flow forced by steric interactions and alignment, a state of active matter that we call "swarming liquid crystal." We develop scaling arguments consistent with this interpretation. PMID:26465513

  4. Turbulence of swarming sperm

    NASA Astrophysics Data System (ADS)

    Creppy, Adama; Praud, Olivier; Druart, Xavier; Kohnke, Philippa L.; Plouraboué, Franck

    2015-09-01

    Collective motion of self-sustained swarming flows has recently provided examples of small-scale turbulence arising where viscous effects are dominant. We report the first observation of universal enstrophy cascade in concentrated swarming sperm consistent with a body of evidence built from various independent measurements. We found a well-defined k-3 power-law decay of a velocity field power spectrum and relative dispersion of small beads consistent with theoretical predictions in 2D turbulence. Concentrated living sperm displays long-range, correlated whirlpool structures of a size that provides an integral scale of turbulence. We propose a consistent explanation for this quasi-2D turbulence based on self-structured laminated flow forced by steric interactions and alignment, a state of active matter that we call "swarming liquid crystal." We develop scaling arguments consistent with this interpretation.

  5. Swarming: flexible roaming plans.

    PubMed

    Partridge, Jonathan D; Harshey, Rasika M

    2013-03-01

    Movement over an agar surface via swarming motility is subject to formidable challenges not encountered during swimming. Bacteria display a great deal of flexibility in coping with these challenges, which include attracting water to the surface, overcoming frictional forces, and reducing surface tension. Bacteria that swarm on "hard" agar surfaces (robust swarmers) display a hyperflagellated and hyperelongated morphology. Bacteria requiring a "softer" agar surface (temperate swarmers) do not exhibit such a dramatic morphology. For polarly flagellated robust swarmers, there is good evidence that restriction of flagellar rotation somehow signals the induction of a large number of lateral flagella, but this scenario is apparently not relevant to temperate swarmers. Swarming bacteria can be further subdivided by their requirement for multiple stators (Mot proteins) or a stator-associated protein (FliL), secretion of essential polysaccharides, cell density-dependent gene regulation including surfactant synthesis, a functional chemotaxis signaling pathway, appropriate cyclic (c)-di-GMP levels, induction of virulence determinants, and various nutritional requirements such as iron limitation or nitrate availability. Swarming strategies are as diverse as the bacteria that utilize them. The strength of these numerous designs stems from the vantage point they offer for understanding mechanisms for effective colonization of surface niches, acquisition of pathogenic potential, and identification of environmental signals that regulate swarming. The signature swirling and streaming motion within a swarm is an interesting phenomenon in and of itself, an emergent behavior with properties similar to flocking behavior in diverse systems, including birds and fish, providing a convenient new avenue for modeling such behavior. PMID:23264580

  6. Swarming: Flexible Roaming Plans

    PubMed Central

    Partridge, Jonathan D.

    2013-01-01

    Movement over an agar surface via swarming motility is subject to formidable challenges not encountered during swimming. Bacteria display a great deal of flexibility in coping with these challenges, which include attracting water to the surface, overcoming frictional forces, and reducing surface tension. Bacteria that swarm on “hard” agar surfaces (robust swarmers) display a hyperflagellated and hyperelongated morphology. Bacteria requiring a “softer” agar surface (temperate swarmers) do not exhibit such a dramatic morphology. For polarly flagellated robust swarmers, there is good evidence that restriction of flagellar rotation somehow signals the induction of a large number of lateral flagella, but this scenario is apparently not relevant to temperate swarmers. Swarming bacteria can be further subdivided by their requirement for multiple stators (Mot proteins) or a stator-associated protein (FliL), secretion of essential polysaccharides, cell density-dependent gene regulation including surfactant synthesis, a functional chemotaxis signaling pathway, appropriate cyclic (c)-di-GMP levels, induction of virulence determinants, and various nutritional requirements such as iron limitation or nitrate availability. Swarming strategies are as diverse as the bacteria that utilize them. The strength of these numerous designs stems from the vantage point they offer for understanding mechanisms for effective colonization of surface niches, acquisition of pathogenic potential, and identification of environmental signals that regulate swarming. The signature swirling and streaming motion within a swarm is an interesting phenomenon in and of itself, an emergent behavior with properties similar to flocking behavior in diverse systems, including birds and fish, providing a convenient new avenue for modeling such behavior. PMID:23264580

  7. Collective behaviors of two-component swarms.

    PubMed

    You, Sang Koo; Kwon, Dae Hyuk; Park, Yong-ik; Kim, Sun Myong; Chung, Myung-Hoon; Kim, Chul Koo

    2009-12-01

    We present a particle-based simulation study on two-component swarms where there exist two different types of groups in a swarm. Effects of different parameters between the two groups are studied systematically based on Langevin's equation. It is shown that the mass difference can introduce a protective behavior for the lighter members of the swarm in a vortex state. When the self-propelling strength is allowed to differ between two groups, it is observed that the swarm becomes spatially segregated and finally separated into two components at a certain critical value. We also investigate effects of different preferences for shelters on their collective decision making. In particular, it is found that the probability of selecting a shelter from the other varies sigmoidally as a function of the number ratio. The model is shown to describe the dynamics of the shelter choosing process of the cockroach-robot mixed group satisfactorily. It raises the possibility that the present model can be applied to the problems of pest control and fishing using robots and decoys. PMID:19716374

  8. Collective Patterns of Swarm Dynamics and the Lyapunov Analysis of Individual Behaviors

    NASA Astrophysics Data System (ADS)

    Shiraishi, Masashi; Aizawa, Yoji

    2015-05-01

    In this report, the collective patterns of swarms dynamics are theoretically studied using a self-propelled particle model, and the dynamics of the individual particles in the swarms is precisely studied using Lyapunov analysis. Furthermore, the collective behaviors of the swarms are classified into several patterns characterized by the following statistical quantities: Lyapunov spectrum, Lyapunov dimension, and stability and instability indices. Consequently, we propose individual instability exponents (IIEs) that describes the sensitivity of the individual particles in the swarm, and show that the IIEs is a significant order parameter that demonstrates changes in the pattern and expresses the activity of the swarm dynamics. Furthermore, we show that the mean and variance obtained after calculating the speed distribution of individual particles reveal marked variations corresponding to the change in the pattern in a manner similar to those of the IIEs.

  9. Particle impact on metal substrates with application to foreign object damage to aircraft engines

    NASA Astrophysics Data System (ADS)

    Chen, Xi; Hutchinson, John W.

    2002-12-01

    Foreign object damage (FOD) occurs when hard, millimeter-sized objects such as gravel or sand are ingested into aircraft jet engines. Particles impacting turbine blades at velocities up to about 300 m/ s produce small indentation craters which can become sites for fatigue crack initiation, severely limiting the lifetime of the blade. A framework for analyzing FOD and its effect on fatigue cracking is established in this paper. Finite element analysis is used to determine the residual stresses and geometric stress concentration resulting from FOD. The roles of material rate sensitivity and inertia are delineated. The most important non-dimensional parameters governing impact indents are identified, significantly reducing the set of independent parameters. The second step in the analysis focuses on the potency of cracks emerging from critical locations at the indents. The results have been used to address the question: When and to what extent do the residual stresses and stress concentration caused by FOD reduce the critical crack size associated with threshold fatigue crack growth? For deep indents, it is found that elastic stress concentration is the dominant factor in reducing critical crack threshold when the applied cyclic load ratio, R, is large, otherwise the residual stresses are also important. Comparisons with a set of experiments conducted in parallel with the theory show that the numerical approach can account for various phenomena observed in practice.

  10. Delay-induced instabilities in self-propelling swarms.

    PubMed

    Forgoston, Eric; Schwartz, Ira B

    2008-03-01

    We consider a general model of self-propelling particles interacting through a pairwise attractive force in the presence of noise and communication time delay. Previous work by Erdmann [Phys. Rev. E 71, 051904 (2005)] has shown that a large enough noise intensity will cause a translating swarm of individuals to transition to a rotating swarm with a stationary center of mass. We show that with the addition of a time delay, the model possesses a transition that depends on the size of the coupling amplitude. This transition is independent of the initial swarm state (traveling or rotating) and is characterized by the alignment of all of the individuals along with a swarm oscillation. By considering the mean field equations without noise, we show that the time-delay-induced transition is associated with a Hopf bifurcation. The analytical result yields good agreement with numerical computations of the value of the coupling parameter at the Hopf point. PMID:18517450

  11. Delay-induced instabilities in self-propelling swarms

    NASA Astrophysics Data System (ADS)

    Forgoston, Eric; Schwartz, Ira B.

    2008-03-01

    We consider a general model of self-propelling particles interacting through a pairwise attractive force in the presence of noise and communication time delay. Previous work by Erdmann [Phys. Rev. E 71, 051904 (2005)] has shown that a large enough noise intensity will cause a translating swarm of individuals to transition to a rotating swarm with a stationary center of mass. We show that with the addition of a time delay, the model possesses a transition that depends on the size of the coupling amplitude. This transition is independent of the initial swarm state (traveling or rotating) and is characterized by the alignment of all of the individuals along with a swarm oscillation. By considering the mean field equations without noise, we show that the time-delay-induced transition is associated with a Hopf bifurcation. The analytical result yields good agreement with numerical computations of the value of the coupling parameter at the Hopf point.

  12. Delay-induced instabilities in self-propelling swarms

    PubMed Central

    Forgoston, Eric; Schwartz, Ira B.

    2013-01-01

    We consider a general model of self-propelling particles interacting through a pairwise attractive force in the presence of noise and communication time delay. Previous work by Erdmann et al. [Phys. Rev. E 71, 051904 (2005)] has shown that a large enough noise intensity will cause a translating swarm of individuals to transition to a rotating swarm with a stationary center of mass. We show that with the addition of a time delay, the model possesses a transition that depends on the size of the coupling amplitude. This transition is independent of the initial swarm state (traveling or rotating) and is characterized by the alignment of all of the individuals along with a swarm oscillation. By considering the mean field equations without noise, we show that the time-delay-induced transition is associated with a Hopf bifurcation. The analytical result yields good agreement with numerical computations of the value of the coupling parameter at the Hopf point. PMID:18517450

  13. Analysis of image thresholding segmentation algorithms based on swarm intelligence

    NASA Astrophysics Data System (ADS)

    Zhang, Yi; Lu, Kai; Gao, Yinghui; Yang, Bo

    2013-03-01

    Swarm intelligence-based image thresholding segmentation algorithms are playing an important role in the research field of image segmentation. In this paper, we briefly introduce the theories of four existing image segmentation algorithms based on swarm intelligence including fish swarm algorithm, artificial bee colony, bacteria foraging algorithm and particle swarm optimization. Then some image benchmarks are tested in order to show the differences of the segmentation accuracy, time consumption, convergence and robustness for Salt & Pepper noise and Gaussian noise of these four algorithms. Through these comparisons, this paper gives qualitative analyses for the performance variance of the four algorithms. The conclusions in this paper would give a significant guide for the actual image segmentation.

  14. Searching for effective forces in laboratory insect swarms

    NASA Astrophysics Data System (ADS)

    Puckett, James G.; Kelley, Douglas H.; Ouellette, Nicholas T.

    2014-04-01

    Collective animal behaviour is often modeled by systems of agents that interact via effective social forces, including short-range repulsion and long-range attraction. We search for evidence of such effective forces by studying laboratory swarms of the flying midge Chironomus riparius. Using multi-camera stereoimaging and particle-tracking techniques, we record three-dimensional trajectories for all the individuals in the swarm. Acceleration measurements show a clear short-range repulsion, which we confirm by considering the spatial statistics of the midges, but no conclusive long-range interactions. Measurements of the mean free path of the insects also suggest that individuals are on average very weakly coupled, but that they are also tightly bound to the swarm itself. Our results therefore suggest that some attractive interaction maintains cohesion of the swarms, but that this interaction is not as simple as an attraction to nearest neighbours.

  15. A smooth particle hydrodynamics code to model collisions between solid, self-gravitating objects

    NASA Astrophysics Data System (ADS)

    Schäfer, C.; Riecker, S.; Maindl, T. I.; Speith, R.; Scherrer, S.; Kley, W.

    2016-05-01

    Context. Modern graphics processing units (GPUs) lead to a major increase in the performance of the computation of astrophysical simulations. Owing to the different nature of GPU architecture compared to traditional central processing units (CPUs) such as x86 architecture, existing numerical codes cannot be easily migrated to run on GPU. Here, we present a new implementation of the numerical method smooth particle hydrodynamics (SPH) using CUDA and the first astrophysical application of the new code: the collision between Ceres-sized objects. Aims: The new code allows for a tremendous increase in speed of astrophysical simulations with SPH and self-gravity at low costs for new hardware. Methods: We have implemented the SPH equations to model gas, liquids and elastic, and plastic solid bodies and added a fragmentation model for brittle materials. Self-gravity may be optionally included in the simulations and is treated by the use of a Barnes-Hut tree. Results: We find an impressive performance gain using NVIDIA consumer devices compared to our existing OpenMP code. The new code is freely available to the community upon request. If you are interested in our CUDA SPH code miluphCUDA, please write an email to Christoph Schäfer. miluphCUDA is the CUDA port of miluph. miluph is pronounced [maßl2v]. We do not support the use of the code for military purposes.

  16. A smooth particle hydrodynamics code to model collisions between solid, self-gravitating objects

    NASA Astrophysics Data System (ADS)

    Schäfer, C.; Riecker, S.; Maindl, T. I.; Speith, R.; Scherrer, S.; Kley, W.

    2016-04-01

    Context. Modern graphics processing units (GPUs) lead to a major increase in the performance of the computation of astrophysical simulations. Owing to the different nature of GPU architecture compared to traditional central processing units (CPUs) such as x86 architecture, existing numerical codes cannot be easily migrated to run on GPU. Here, we present a new implementation of the numerical method smooth particle hydrodynamics (SPH) using CUDA and the first astrophysical application of the new code: the collision between Ceres-sized objects. Aims: The new code allows for a tremendous increase in speed of astrophysical simulations with SPH and self-gravity at low costs for new hardware. Methods: We have implemented the SPH equations to model gas, liquids and elastic, and plastic solid bodies and added a fragmentation model for brittle materials. Self-gravity may be optionally included in the simulations and is treated by the use of a Barnes-Hut tree. Results: We find an impressive performance gain using NVIDIA consumer devices compared to our existing OpenMP code. The new code is freely available to the community upon request. If you are interested in our CUDA SPH code miluphCUDA, please write an email to Christoph Schäfer. miluphCUDA is the CUDA port of miluph. miluph is pronounced [maßl2v]. We do not support the use of the code for military purposes.

  17. Artificial neural network and particle swarm optimization for removal of methyl orange by gold nanoparticles loaded on activated carbon and Tamarisk

    NASA Astrophysics Data System (ADS)

    Ghaedi, M.; Ghaedi, A. M.; Ansari, A.; Mohammadi, F.; Vafaei, A.

    2014-11-01

    The influence of variables, namely initial dye concentration, adsorbent dosage (g), stirrer speed (rpm) and contact time (min) on the removal of methyl orange (MO) by gold nanoparticles loaded on activated carbon (Au-NP-AC) and Tamarisk were investigated using multiple linear regression (MLR) and artificial neural network (ANN) and the variables were optimized by partial swarm optimization (PSO). Comparison of the results achieved using proposed models, showed the ANN model was better than the MLR model for prediction of methyl orange removal using Au-NP-AC and Tamarisk. Using the optimal ANN model the coefficient of determination (R2) for the test data set were 0.958 and 0.989; mean squared error (MSE) values were 0.00082 and 0.0006 for Au-NP-AC and Tamarisk adsorbent, respectively. In this study a novel and green approach were reported for the synthesis of gold nanoparticle and activated carbon by Tamarisk. This material was characterized using different techniques such as SEM, TEM, XRD and BET. The usability of Au-NP-AC and activated carbon (AC) Tamarisk for the methyl orange from aqueous solutions was investigated. The effect of variables such as pH, initial dye concentration, adsorbent dosage (g) and contact time (min) on methyl orange removal were studied. Fitting the experimental equilibrium data to various isotherm models such as Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show the suitability and applicability of the Langmuir model. Kinetic models such as pseudo-first order, pseudo-second order, Elovich and intraparticle diffusion models indicate that the second-order equation and intraparticle diffusion models control the kinetic of the adsorption process. The small amount of proposed Au-NP-AC and activated carbon (0.015 g and 0.75 g) is applicable for successful removal of methyl orange (>98%) in short time (20 min for Au-NP-AC and 45 min for Tamarisk-AC) with high adsorption capacity 161 mg g-1 for Au-NP-AC and 3.84 mg g-1 for Tamarisk-AC.

  18. Artificial neural network and particle swarm optimization for removal of methyl orange by gold nanoparticles loaded on activated carbon and Tamarisk.

    PubMed

    Ghaedi, M; Ghaedi, A M; Ansari, A; Mohammadi, F; Vafaei, A

    2014-11-11

    The influence of variables, namely initial dye concentration, adsorbent dosage (g), stirrer speed (rpm) and contact time (min) on the removal of methyl orange (MO) by gold nanoparticles loaded on activated carbon (Au-NP-AC) and Tamarisk were investigated using multiple linear regression (MLR) and artificial neural network (ANN) and the variables were optimized by partial swarm optimization (PSO). Comparison of the results achieved using proposed models, showed the ANN model was better than the MLR model for prediction of methyl orange removal using Au-NP-AC and Tamarisk. Using the optimal ANN model the coefficient of determination (R2) for the test data set were 0.958 and 0.989; mean squared error (MSE) values were 0.00082 and 0.0006 for Au-NP-AC and Tamarisk adsorbent, respectively. In this study a novel and green approach were reported for the synthesis of gold nanoparticle and activated carbon by Tamarisk. This material was characterized using different techniques such as SEM, TEM, XRD and BET. The usability of Au-NP-AC and activated carbon (AC) Tamarisk for the methyl orange from aqueous solutions was investigated. The effect of variables such as pH, initial dye concentration, adsorbent dosage (g) and contact time (min) on methyl orange removal were studied. Fitting the experimental equilibrium data to various isotherm models such as Langmuir, Freundlich, Tempkin and Dubinin-Radushkevich models show the suitability and applicability of the Langmuir model. Kinetic models such as pseudo-first order, pseudo-second order, Elovich and intraparticle diffusion models indicate that the second-order equation and intraparticle diffusion models control the kinetic of the adsorption process. The small amount of proposed Au-NP-AC and activated carbon (0.015 g and 0.75 g) is applicable for successful removal of methyl orange (>98%) in short time (20 min for Au-NP-AC and 45 min for Tamarisk-AC) with high adsorption capacity 161 mg g(-1) for Au-NP-AC and 3.84 mg g(-1) for

  19. A comprehensive review of swarm optimization algorithms.

    PubMed

    Ab Wahab, Mohd Nadhir; Nefti-Meziani, Samia; Atyabi, Adham

    2015-01-01

    Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655

  20. A Comprehensive Review of Swarm Optimization Algorithms

    PubMed Central

    2015-01-01

    Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655

  1. LinkMind: link optimization in swarming mobile sensor networks.

    PubMed

    Ngo, Trung Dung

    2011-01-01

    A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation. PMID:22164070

  2. LinkMind: Link Optimization in Swarming Mobile Sensor Networks

    PubMed Central

    Ngo, Trung Dung

    2011-01-01

    A swarming mobile sensor network is comprised of a swarm of wirelessly connected mobile robots equipped with various sensors. Such a network can be applied in an uncertain environment for services such as cooperative navigation and exploration, object identification and information gathering. One of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm is demonstrated and evaluated in simulation. PMID:22164070

  3. Ethiopian Tertiary dike swarms

    NASA Technical Reports Server (NTRS)

    Mohr, P. A.

    1971-01-01

    Mapping of the Ethiopian rift and Afar margins revealed the existence of Tertiary dike swarms. The structural relations of these swarms and the fed lava pile to monoclinal warping of the margins partly reflect a style of continental margin tectonics found in other parts of the world. In Ethiopia, however, conjugate dike trends appear to be unusually strongly developed. Relation of dikes to subsequent margin faulting is ambiguous, and there are instances where the two phenomena are spatially separate and of differing trends. There is no evidence for lateral migration with time of dike injection toward the rift zone. No separate impingement of Red Sea, Gulf of Aden, and African rift system stress fields on the Ethiopian region can be demonstrated from the Tertiary dike swarms. Rather, a single, regional paleostress field existed, suggestive of a focus beneath the central Ethiopian plateau. This stress field was dominated by tension: there is no cogent evidence for shearing along the rift margins. A gentle compression along the rift floor is indicated. A peculiar sympathy of dike hade directions at given localities is evident.

  4. Swarm robotics and minimalism

    NASA Astrophysics Data System (ADS)

    Sharkey, Amanda J. C.

    2007-09-01

    Swarm Robotics (SR) is closely related to Swarm Intelligence, and both were initially inspired by studies of social insects. Their guiding principles are based on their biological inspiration and take the form of an emphasis on decentralized local control and communication. Earlier studies went a step further in emphasizing the use of simple reactive robots that only communicate indirectly through the environment. More recently SR studies have moved beyond these constraints to explore the use of non-reactive robots that communicate directly, and that can learn and represent their environment. There is no clear agreement in the literature about how far such extensions of the original principles could go. Should there be any limitations on the individual abilities of the robots used in SR studies? Should knowledge of the capabilities of social insects lead to constraints on the capabilities of individual robots in SR studies? There is a lack of explicit discussion of such questions, and researchers have adopted a variety of constraints for a variety of reasons. A simple taxonomy of swarm robotics is presented here with the aim of addressing and clarifying these questions. The taxonomy distinguishes subareas of SR based on the emphases and justifications for minimalism and individual simplicity.

  5. An improved cockroach swarm optimization.

    PubMed

    Obagbuwa, I C; Adewumi, A O

    2014-01-01

    Hunger component is introduced to the existing cockroach swarm optimization (CSO) algorithm to improve its searching ability and population diversity. The original CSO was modelled with three components: chase-swarming, dispersion, and ruthless; additional hunger component which is modelled using partial differential equation (PDE) method is included in this paper. An improved cockroach swarm optimization (ICSO) is proposed in this paper. The performance of the proposed algorithm is tested on well known benchmarks and compared with the existing CSO, modified cockroach swarm optimization (MCSO), roach infestation optimization RIO, and hungry roach infestation optimization (HRIO). The comparison results show clearly that the proposed algorithm outperforms the existing algorithms. PMID:24959611

  6. Coherent Pattern Prediction in Swarms of Delay-Coupled Agents

    NASA Astrophysics Data System (ADS)

    Mier-Y-Teran-Romero, Luis; Forgoston, Eric; Scwartz, Ira

    2013-03-01

    We consider a general swarm model of self-propelling particles interacting through a pairwise potential in the presence of a fixed communication time delay. Previous work has shown that swarms with communication time delays and noise may display pattern transitions that depend on the size of the coupling amplitude. We extend these results by completely unfolding the bifurcation structure of the mean field approximation. Our analysis reveals a direct correspondence between the different dynamical behaviors found in different regions of the coupling-time delay plane with the different classes of simulated coherent swarm patterns. We derive the spatio-temporal scales of the swarm structures, and also demonstrate how the complicated interplay of coupling strength, time delay, noise intensity, and choice of initial conditions can affect the swarm. In addition, when adding noise to the system, we find that for sufficiently large values of the coupling strength and/or the time delay, there is a noise intensity threshold that forces a transition of the swarm from a misaligned state into an aligned state. We show that this alignment transition exhibits hysteresis when the noise intensity is taken to be time dependent. Office of Naval Research, NIH (LMR and IBS) and NRL (EF)

  7. Features of Bacillus cereus swarm cells.

    PubMed

    Senesi, Sonia; Salvetti, Sara; Celandroni, Francesco; Ghelardi, Emilia

    2010-11-01

    When propagated on solid surfaces, Bacillus cereus can produce differentiated swarm cells under a wide range of growth conditions. This behavioural versatility is ecologically relevant, since it allows this bacterium to adapt swarming to environmental changes. Swarming by B. cereus is medically important: swarm cells are more virulent and particularly prone to invade host tissues. Characterisation of swarming-deficient mutants highlights that flagellar genes as well as genes governing different metabolic pathways are involved in swarm-cell differentiation. In this review, the environmental and genetic requirements for swarming and the role played by swarm cells in the virulence this pathogen exerts will be outlined. PMID:21035546

  8. Position-adaptive explosive detection concepts for swarming micro-UAVs

    NASA Astrophysics Data System (ADS)

    Selmic, Rastko R.; Mitra, Atindra

    2008-04-01

    We have formulated a series of position-adaptive sensor concepts for explosive detection applications using swarms of micro-UAV's. These concepts are a generalization of position-adaptive radar concepts developed for challenging conditions such as urban environments. For radar applications, this concept is developed with platforms within a UAV swarm that spatially-adapt to signal leakage points on the perimeter of complex clutter environments to collect information on embedded objects-of-interest. The concept is generalized for additional sensors applications by, for example, considering a wooden cart that contains explosives. We can formulate system-of-systems concepts for a swarm of micro-UAV's in an effort to detect whether or not a given cart contains explosives. Under this new concept, some of the members of the UAV swarm can serve as position-adaptive "transmitters" by blowing air over the cart and some of the members of the UAV swarm can serve as position-adaptive "receivers" that are equipped with chem./bio sensors that function as "electronic noses". The final objective can be defined as improving the particle count for the explosives in the air that surrounds a cart via development of intelligent position-adaptive control algorithms in order to improve the detection and false-alarm statistics. We report on recent simulation results with regard to designing optimal sensor placement for explosive or other chemical agent detection. This type of information enables the development of intelligent control algorithms for UAV swarm applications and is intended for the design of future system-of-systems with adaptive intelligence for advanced surveillance of unknown regions. Results are reported as part of a parametric investigation where it is found that the probability of contaminant detection depends on the air flow that carries contaminant particles, geometry of the surrounding space, leakage areas, and other factors. We present a concept of position

  9. From hybrid swarms to swarms of hybrids

    USGS Publications Warehouse

    Stohlgren, Thomas J.; Szalanski, Allen L; Gaskin, John F.; Young, Nicholas E.; West, Amanda; Jarnevich, Catherine S.; Tripodi, Amber

    2015-01-01

    Science has shown that the introgression or hybridization of modern humans (Homo sapiens) with Neanderthals up to 40,000 YBP may have led to the swarm of modern humans on earth. However, there is little doubt that modern trade and transportation in support of the humans has continued to introduce additional species, genotypes, and hybrids to every country on the globe. We assessed the utility of species distributions modeling of genotypes to assess the risk of current and future invaders. We evaluated 93 locations of the genus Tamarix for which genetic data were available. Maxent models of habitat suitability showed that the hybrid, T. ramosissima x T. chinensis, was slightly greater than the parent taxa (AUCs > 0.83). General linear models of Africanized honey bees, a hybrid cross of Tanzanian Apis mellifera scutellata and a variety of European honey bee including A. m. ligustica, showed that the Africanized bees (AUC = 0.81) may be displacing European honey bees (AUC > 0.76) over large areas of the southwestern U.S. More important, Maxent modeling of sub-populations (A1 and A26 mitotypes based on mDNA) could be accurately modeled (AUC > 0.9), and they responded differently to environmental drivers. This suggests that rapid evolutionary change may be underway in the Africanized bees, allowing the bees to spread into new areas and extending their total range. Protecting native species and ecosystems may benefit from risk maps of harmful invasive species, hybrids, and genotypes.

  10. Cell-Division Behavior in a Heterogeneous Swarm Environment.

    PubMed

    Erskine, Adam; Herrmann, J Michael

    2015-01-01

    We present a system of virtual particles that interact using simple kinetic rules. It is known that heterogeneous mixtures of particles can produce particularly interesting behaviors. Here we present a two-species three-dimensional swarm in which a behavior emerges that resembles cell division. We show that the dividing behavior exists across a narrow but finite band of parameters and for a wide range of population sizes. When executed in a two-dimensional environment the swarm's characteristics and dynamism manifest differently. In further experiments we show that repeated divisions can occur if the system is extended by a biased equilibrium process to control the split of populations. We propose that this repeated division behavior provides a simple model for cell-division mechanisms and is of interest for the formation of morphological structure and to swarm robotics. PMID:26545164

  11. Behavioural Rule Discovery from Swarm Systems

    NASA Astrophysics Data System (ADS)

    Stoops, David; Wang, Hui; Moore, George; Bi, Yaxin

    Rules determine the functionality of a given system, in either natural or man-made systems. Man-made systems, such as computer applications, use a set of known rules to control the behaviours applied in a strict manner. Biological or natural systems employ unknown rules, these being undiscovered rules which are more complex. These rules are unknown due to the inability to determine how they are applied, unless observed by a third party. The swarm is one of the largest naturally observed systems, with bird flocks and ant colonies being the most notable. It is a collection or group of individuals who use behaviours to complete a given goal or objective. It is the aim of this paper to present rule discovery methods for the mining of these unknown rules within a swarm system, employing a bird flock simulation environment to gather data.

  12. DEMOCRITUS: An adaptive particle in cell (PIC) code for object-plasma interactions

    NASA Astrophysics Data System (ADS)

    Lapenta, Giovanni

    2011-06-01

    A new method for the simulation of plasma materials interactions is presented. The method is based on the particle in cell technique for the description of the plasma and on the immersed boundary method for the description of the interactions between materials and plasma particles. A technique to adapt the local number of particles and grid adaptation are used to reduce the truncation error and the noise of the simulations, to increase the accuracy per unit cost. In the present work, the computational method is verified against known results. Finally, the simulation method is applied to a number of specific examples of practical scientific and engineering interest.

  13. A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon.

    PubMed

    Ghaedi, M; Ansari, A; Bahari, F; Ghaedi, A M; Vafaei, A

    2015-02-25

    In the present study, zinc sulfide nanoparticle loaded on activated carbon (ZnS-NP-AC) simply was synthesized in the presence of ultrasound and characterized using different techniques such as SEM and BET analysis. Then, this material was used for brilliant green (BG) removal. To dependency of BG removal percentage toward various parameters including pH, adsorbent dosage, initial dye concentration and contact time were examined and optimized. The mechanism and rate of adsorption was ascertained by analyzing experimental data at various time to conventional kinetic models such as pseudo-first-order and second order, Elovich and intra-particle diffusion models. Comparison according to general criterion such as relative error in adsorption capacity and correlation coefficient confirm the usability of pseudo-second-order kinetic model for explanation of data. The Langmuir models is efficiently can explained the behavior of adsorption system to give full information about interaction of BG with ZnS-NP-AC. A multiple linear regression (MLR) and a hybrid of artificial neural network and partial swarm optimization (ANN-PSO) model were used for prediction of brilliant green adsorption onto ZnS-NP-AC. Comparison of the results obtained using offered models confirm higher ability of ANN model compare to the MLR model for prediction of BG adsorption onto ZnS-NP-AC. Using the optimal ANN-PSO model the coefficient of determination (R(2)) were 0.9610 and 0.9506; mean squared error (MSE) values were 0.0020 and 0.0022 for the training and testing data set, respectively. PMID:25286113

  14. A hybrid artificial neural network and particle swarm optimization for prediction of removal of hazardous dye brilliant green from aqueous solution using zinc sulfide nanoparticle loaded on activated carbon

    NASA Astrophysics Data System (ADS)

    Ghaedi, M.; Ansari, A.; Bahari, F.; Ghaedi, A. M.; Vafaei, A.

    2015-02-01

    In the present study, zinc sulfide nanoparticle loaded on activated carbon (ZnS-NP-AC) simply was synthesized in the presence of ultrasound and characterized using different techniques such as SEM and BET analysis. Then, this material was used for brilliant green (BG) removal. To dependency of BG removal percentage toward various parameters including pH, adsorbent dosage, initial dye concentration and contact time were examined and optimized. The mechanism and rate of adsorption was ascertained by analyzing experimental data at various time to conventional kinetic models such as pseudo-first-order and second order, Elovich and intra-particle diffusion models. Comparison according to general criterion such as relative error in adsorption capacity and correlation coefficient confirm the usability of pseudo-second-order kinetic model for explanation of data. The Langmuir models is efficiently can explained the behavior of adsorption system to give full information about interaction of BG with ZnS-NP-AC. A multiple linear regression (MLR) and a hybrid of artificial neural network and partial swarm optimization (ANN-PSO) model were used for prediction of brilliant green adsorption onto ZnS-NP-AC. Comparison of the results obtained using offered models confirm higher ability of ANN model compare to the MLR model for prediction of BG adsorption onto ZnS-NP-AC. Using the optimal ANN-PSO model the coefficient of determination (R2) were 0.9610 and 0.9506; mean squared error (MSE) values were 0.0020 and 0.0022 for the training and testing data set, respectively.

  15. Components of Swarm Intelligence

    SciTech Connect

    David Bruemmer; Donald Dudenhoeffer; Matthew Anderson; Mark McKay

    2004-03-01

    This paper discusses the successes and failures over the past three years as efforts at the Idaho National Engineering and Environmental Laboratory (INEEL) have developed and evaluated robot behaviors that promote the emergence of swarm intelligence. Using a team of 12 small robots with the ability to respond to light and sound, the INEEL has investigated the fundamental advantages of swarm behavior as well as the limitations of this approach. The paper discusses the ways in which biology has inspired this work and the ways in which adherence to the biological model has proven to be both a benefit and hindrance to developing a fieldable system. The paper outlines how a hierarchical command and control structure can be imposed in order to permit human control at a level of group abstraction and discusses experimental results that show how group performance scales as different numbers of robots are utilized. Lastly, the paper outlines the applications for which the resulting capabilities have been applied and demonstrated.

  16. Swarming rings of bacteria.

    NASA Astrophysics Data System (ADS)

    Brenner, M. P.; Levitov, L. S.

    1996-03-01

    The behavior of bacterii controlled by chemotaxis can lead to a complicated spatial organization, producing swarming rings, and steady or moving aggregates( E. O. Budrene, and H. C. Berg, Complex patterns formed by motile cells of Escherichia coli. Nature 349, 630-633 (1991). ). We present a simple theory that explains the experimentally observed structures, by solving analytically two coupled differential equations, for the densities of bacterii and of chemoattractant. The equations have an interesting relation to the exactly solvable Burgers equation, and admit soliton-like solutions, that can be steady or moving. In addition, we find that there are singular solutions to the equations in which the bacterial density diverges. The theory agrees very well with the experiment: the solitons correspond to the observed travelling rings, the singularities describe formation of aggregates. In particular, the theory explains why the velocity of swarming rings decreases with the increase of the food concentration, the fact apparently not accounted by other existing approaches( L. Tsimring et. al., Phys. Rev. Lett., 75, 1859 (1995); Woodward, et al, Biophysical Journal, 68, 2181-2189 (1995). ).

  17. Swarming by Nature and by Design

    NASA Astrophysics Data System (ADS)

    Bertozzi, Andrea

    2007-03-01

    The cohesive movement of a biological population is a commonly observed natural phenomenon. With the advent of platforms of unmanned vehicles, this occurrence is attracting renewed interest from the engineering community. This talk will review recent research results on modeling and analysis of biological swarms with some connection to the design ideas for efficient algorithms to control groups of autonomous agents. For biological models we consider two kinds of systems: driven particle systems based on force laws and continuum models based on kinematic and dynamic rules. Both models involve long-rage social attraction and short range dispersal and yield patterns involving clumping, mill vortices, and surface-tension-like effects.

  18. Multiscale Model of Swarming Bacteria

    NASA Astrophysics Data System (ADS)

    Alber, Mark

    2011-03-01

    Many bacteria can rapidly traverse surfaces from which they are extracting nutrient for growth. They generate flat, spreading colonies, called swarms because they resemble swarms of insects. In the beginning of the talk, swarms of the M. xanthus will be described in detail. Individual M. xanthus cells are elongated; they always move in the direction of their long axis; and they are in constant motion, repeatedly touching each other. As a cell glides, the slime capsule of a cell interacts with the bare agar surface, non-oriented slime which arises from the surface contact with the slime capsule, or oriented slime trails. Remarkably, cells regularly reverse their gliding directions. In this talk a detailed cell- and behavior-based computational model of M. xanthus swarming will be used to demonstrate that reversals of gliding direction and cell bending are essential for swarming and that specific reversal frequencies result in optimal swarming rate of the whole population. This suggests that the circuit regulating reversals evolved to its current sensitivity under selection for growth achieved by swarming.

  19. On collision processes in a compressed layer, involving waterdrops and solid particles and a moving object

    NASA Astrophysics Data System (ADS)

    Dykhovskii, I. A.; Kovalev, P. I.

    Supersonic heterogeneous gas flows colliding with blunt-nosed objects are examined by studying the effects of water droplets colliding with similar objects. Various optical techniques are applied to the study of the effects to capitalize on the advantages and efficiency of each. The patterns of disturbance are visualized and described qualitatively to isolate the mechanisms of interaction in compressed layers.

  20. Effects of heavy particle irradiation on diet on object recognition memory in rats

    Technology Transfer Automated Retrieval System (TEKTRAN)

    On long duration missions to other planets astronauts will be exposed to types and doses of radiation that are not experienced in low earth orbit. Previous research using a ground-based model for exposure to cosmic rays has shown that exposure to heavy particles, such as 56Fe, disrupts spatial learn...

  1. Intrinsic fluctuations and driven response of insect swarms

    NASA Astrophysics Data System (ADS)

    Ni, Rui; Puckett, James G.; Dufresne, Eric R.; Ouellette, Nicholas T.

    2015-03-01

    Much of our understanding of collective behaviour in social animals comes from passive observations of animal groups. To understand the group dynamics fully, however, we must also characterize the response of animal aggregations to disturbances. Using three-dimensional particle tracking, we study both the intrinsic fluctuations of laboratory swarms of the non-biting midge Chironomus riparius and the response of the swarms to controlled external perturbations: the amplitude-modulated sound of male midge wingbeats. Although these perturbations have an insignificant effect on the behavior of individuals, we find that they can have a strong impact on the collective movement. Intriguingly, the response of the swarm is similar reminiscent to of that of a passive equilibrium system to an external driving force, with microscopic fluctuations underlying combining to produce a macroscopic linear response over a wide range of driving frequencies.

  2. Visualization of Biosurfactant Film Flow in a Bacillus subtilis Swarm Colony on an Agar Plate

    PubMed Central

    Kim, Kyunghoon; Kim, Jung Kyung

    2015-01-01

    Collective bacterial dynamics plays a crucial role in colony development. Although many research groups have studied the behavior of fluidic swarm colonies, the detailed mechanics of its motion remains elusive. Here, we developed a visualization method using submicron fluorescent beads for investigating the flow field in a thin layer of fluid that covers a Bacillus subtilis swarm colony growing on an agar plate. The beads were initially embedded in the agar plate and subsequently distributed spontaneously at the upper surface of the expanding colony. We conducted long-term live cell imaging of the B. subtilis colony using the fluorescent tracers, and obtained high-resolution velocity maps of microscale vortices in the swarm colony using particle image velocimetry. A distinct periodic fluctuation in the average speed and vorticity of flow in swarm colony was observed at the inner region of the colony, and correlated with the switch between bacterial swarming and growth phases. At the advancing edge of the colony, both the magnitudes of velocity and vorticity of flow in swarm colony were inversely correlated with the spreading speed of the swarm edge. The advanced imaging tool developed in this study would facilitate further understanding of the effect of micro vortices in swarm colony on the collective dynamics of bacteria. PMID:26343634

  3. Visualization of Biosurfactant Film Flow in a Bacillus subtilis Swarm Colony on an Agar Plate.

    PubMed

    Kim, Kyunghoon; Kim, Jung Kyung

    2015-01-01

    Collective bacterial dynamics plays a crucial role in colony development. Although many research groups have studied the behavior of fluidic swarm colonies, the detailed mechanics of its motion remains elusive. Here, we developed a visualization method using submicron fluorescent beads for investigating the flow field in a thin layer of fluid that covers a Bacillus subtilis swarm colony growing on an agar plate. The beads were initially embedded in the agar plate and subsequently distributed spontaneously at the upper surface of the expanding colony. We conducted long-term live cell imaging of the B. subtilis colony using the fluorescent tracers, and obtained high-resolution velocity maps of microscale vortices in the swarm colony using particle image velocimetry. A distinct periodic fluctuation in the average speed and vorticity of flow in swarm colony was observed at the inner region of the colony, and correlated with the switch between bacterial swarming and growth phases. At the advancing edge of the colony, both the magnitudes of velocity and vorticity of flow in swarm colony were inversely correlated with the spreading speed of the swarm edge. The advanced imaging tool developed in this study would facilitate further understanding of the effect of micro vortices in swarm colony on the collective dynamics of bacteria. PMID:26343634

  4. Flagellar flows around bacterial swarms

    NASA Astrophysics Data System (ADS)

    Dauparas, Justas; Lauga, Eric

    2016-08-01

    Flagellated bacteria on nutrient-rich substrates can differentiate into a swarming state and move in dense swarms across surfaces. A recent experiment measured the flow in the fluid around an Escherichia coli swarm [Wu, Hosu, and Berg, Proc. Natl. Acad. Sci. USA 108, 4147 (2011)], 10.1073/pnas.1016693108. A systematic chiral flow was observed in the clockwise direction (when viewed from above) ahead of the swarm with flow speeds of about 10 μ m /s , about 3 times greater than the radial velocity at the edge of the swarm. The working hypothesis is that this flow is due to the action of cells stalled at the edge of a colony that extend their flagellar filaments outward, moving fluid over the virgin agar. In this work we quantitatively test this hypothesis. We first build an analytical model of the flow induced by a single flagellum in a thin film and then use the model, and its extension to multiple flagella, to compare with experimental measurements. The results we obtain are in agreement with the flagellar hypothesis. The model provides further quantitative insight into the flagella orientations and their spatial distributions as well as the tangential speed profile. In particular, the model suggests that flagella are on average pointing radially out of the swarm and are not wrapped tangentially.

  5. Numerical simulation of particle fluxes formation generated as a result of space objects breakups in orbit

    NASA Astrophysics Data System (ADS)

    Aleksandrova, A. G.; Galushina, T. Yu.

    2015-12-01

    The paper describes the software package developed for the numerical simulation of the breakups of natural and artificial objects and algorithms on which it is based. A new software "Numerical model of breakups" includes models of collapse of the spacecraft (SC) as a result of the explosion and collision as well as two models of the explosion of an asteroid.

  6. Cluster operations in support of SWARM and first results

    NASA Astrophysics Data System (ADS)

    Masson, Arnaud; Escoubet, Philippe; Laakso, Harri

    2015-04-01

    The measure of the Earth's magnetic field is perturbed by the external electric currents flowing in the magnetosphere and the ionosphere. One of the SWARM prime scientific objectives is to investigate the influence of these currents, including the ring current and field aligned currents (FAC). To help disentangle their effects, special operations have been designed and coordinated with the ESA Cluster mission. This unique constellation of four satellites enable the measure of these currents at higher altitudes better than any previous mission. The complementarity of these two missions was foreseen at the beginning of the SWARM mission design, about 10 years ago. The 2014 special operations in support of SWARM is in other words a forward-looking idea becoming reality. We will first briefly recall a few scientific highlights of the Cluster mission, related to the first measure of currents in space by a constellation of four satellites. We will then talk about the 2014 special operations designed and executed in support of SWARM. We will then present future special operations that are planned to be executed by Cluster up to 2018 in support of SWARM. Preliminary results already obtained will be also evoked.

  7. Rao-Blackwellized marginal particle filtering for multiple object tracking in molecular bioimaging.

    PubMed

    Smal, Ihor; Draegestein, Katharina; Galjart, Niels; Niessen, Wiro; Meijering, Erik

    2007-01-01

    Modern live cell fluorescence microscopy imaging systems, used abundantly for studying intra-cellular processes in vivo, generate vast amounts of noisy image data that cannot be processed efficiently and accurately by means of manual or current computerized techniques. We propose an improved tracking method, built within a Bayesian probabilistic framework, which better exploits temporal information and prior knowledge. Experiments on simulated and real fluorescence microscopy image data acquired for microtubule dynamics studies show that the technique is more robust to noise, photobleaching, and object interaction than common tracking methods and yields results that are in good agreement with expert cell biologists. PMID:17633693

  8. Tracking a large number of closely spaced objects based on the particle probability hypothesis density filter via optical sensor

    NASA Astrophysics Data System (ADS)

    Lin, Liangkui; Xu, Hui; An, Wei; Sheng, Weidong; Xu, Dan

    2011-11-01

    This paper presents a novel approach to tracking a large number of closely spaced objects (CSO) in image sequences that is based on the particle probability hypothesis density (PHD) filter and multiassignment data association. First, the particle PHD filter is adopted to eliminate most of the clutters and to estimate multitarget states. In the particle PHD filter, a noniterative multitarget estimation technique is introduced to reliably estimate multitarget states, and an improved birth particle sampling scheme is present to effectively acquire targets among clutters. Then, an integrated track management method is proposed to realize multitarget track continuity. The core of the track management is the track-to-estimation multiassignment association, which relaxes the traditional one-to-one data association restriction due to the unresolved focal plane CSO measurements. Meanwhile, a unified technique of multiple consecutive misses for track deletion is used jointly to cope with the sensitivity of the PHD filter to the missed detections and to eliminate false alarms further, as well as to initiate tracks of large numbers of CSO. Finally, results of two simulations and one experiment show that the proposed approach is feasible and efficient.

  9. Swarm intelligence in bioinformatics: methods and implementations for discovering patterns of multiple sequences.

    PubMed

    Cui, Zhihua; Zhang, Yi

    2014-02-01

    As a promising and innovative research field, bioinformatics has attracted increasing attention recently. Beneath the enormous number of open problems in this field, one fundamental issue is about the accurate and efficient computational methodology that can deal with tremendous amounts of data. In this paper, we survey some applications of swarm intelligence to discover patterns of multiple sequences. To provide a deep insight, ant colony optimization, particle swarm optimization, artificial bee colony and artificial fish swarm algorithm are selected, and their applications to multiple sequence alignment and motif detecting problem are discussed. PMID:24749453

  10. A fast object-oriented Matlab implementation of the Reproducing Kernel Particle Method

    NASA Astrophysics Data System (ADS)

    Barbieri, Ettore; Meo, Michele

    2012-05-01

    Novel numerical methods, known as Meshless Methods or Meshfree Methods and, in a wider perspective, Partition of Unity Methods, promise to overcome most of disadvantages of the traditional finite element techniques. The absence of a mesh makes meshfree methods very attractive for those problems involving large deformations, moving boundaries and crack propagation. However, meshfree methods still have significant limitations that prevent their acceptance among researchers and engineers, namely the computational costs. This paper presents an in-depth analysis of computational techniques to speed-up the computation of the shape functions in the Reproducing Kernel Particle Method and Moving Least Squares, with particular focus on their bottlenecks, like the neighbour search, the inversion of the moment matrix and the assembly of the stiffness matrix. The paper presents numerous computational solutions aimed at a considerable reduction of the computational times: the use of kd-trees for the neighbour search, sparse indexing of the nodes-points connectivity and, most importantly, the explicit and vectorized inversion of the moment matrix without using loops and numerical routines.

  11. The use of nano-particles to produce iridescent metallic effects on ancient ceramic objects.

    PubMed

    Fermo, P; Padeletti, G

    2012-11-01

    Nano-sized materials have been often used in the past to realize objects with particular characteristics. One of the most outstanding examples is represented by luster pottery, showing shining surfaces with particular optical properties. Luster was one of the most sophisticated technique for the decoration of majolicas. It consists of a thin metallic film containing silver, copper and other substances, like iron oxide and cinnabar, applied in a reducing atmosphere on a previously glazed ceramic. In such a way, beautiful iridescent reflections of different colours (in particular gold and ruby-red) are obtained. This technique, at first developed in Iraq, was introduced in Italy from Spain. In Italy the potters of the two centres of Gubbio and Deruta, in central Italy, became so expert that nowadays modern artisans are not able to reproduce the wonderful effects obtained during Renaissance. A complete characterization by means of numerous techniques has been carried out on a great number of shards and precious work of arts conserved in many important museums. This allowed to draw some correlations between the preparation technique and the obtained nano-structure. PMID:23421282

  12. Singularities and symmetry breaking in swarms.

    PubMed

    Li, Wei; Zhang, Hai-Tao; Chen, Michael Zhi Qiang; Zhou, Tao

    2008-02-01

    A large-scale system consisting of self-propelled particles, moving under the directional alignment rule (DAR), can often self-organize to an ordered state that emerges from an initially rotationally symmetric configuration. It is commonly accepted that the DAR, which leads to effective long-range interactions, is the underlying mechanism contributing to the collective motion. However, in this paper, we demonstrate that a swarm under the DAR has unperceived and inherent singularities. Furthermore, we show that the compelled symmetry-breaking effects at or near the singularities, as well as the topological connectivity of the swarm in the evolution process, contribute fundamentally to the emergence of the collective behavior; and the elimination or weakening of singularities in the DAR will induce an unexpected sharp transition from coherent movement to isotropic dispersion. These results provide some insights into the fundamental issue of collective dynamics: What is the underlying mechanism causing the spontaneous symmetry breaking and leading to eventual coherent motion? PMID:18352064

  13. Collective Behaviour without Collective Order in Wild Swarms of Midges

    PubMed Central

    Attanasi, Alessandro; Cavagna, Andrea; Del Castello, Lorenzo; Giardina, Irene; Melillo, Stefania; Parisi, Leonardo; Pohl, Oliver; Rossaro, Bruno; Shen, Edward; Silvestri, Edmondo; Viale, Massimiliano

    2014-01-01

    Collective behaviour is a widespread phenomenon in biology, cutting through a huge span of scales, from cell colonies up to bird flocks and fish schools. The most prominent trait of collective behaviour is the emergence of global order: individuals synchronize their states, giving the stunning impression that the group behaves as one. In many biological systems, though, it is unclear whether global order is present. A paradigmatic case is that of insect swarms, whose erratic movements seem to suggest that group formation is a mere epiphenomenon of the independent interaction of each individual with an external landmark. In these cases, whether or not the group behaves truly collectively is debated. Here, we experimentally study swarms of midges in the field and measure how much the change of direction of one midge affects that of other individuals. We discover that, despite the lack of collective order, swarms display very strong correlations, totally incompatible with models of non-interacting particles. We find that correlation increases sharply with the swarm's density, indicating that the interaction between midges is based on a metric perception mechanism. By means of numerical simulations we demonstrate that such growing correlation is typical of a system close to an ordering transition. Our findings suggest that correlation, rather than order, is the true hallmark of collective behaviour in biological systems. PMID:25057853

  14. Swarming behavior of gradient-responsive colloids with chemical signaling.

    PubMed

    Grančič, Peter; Štěpánek, František

    2013-07-01

    The article describes swarm dynamics of a system composed of colloidal particles that release chemical signals to navigate their peers toward the location of a static point target in two dimensions. The time evolution of the system is calculated by employing a combination of Brownian dynamics method for the particle motion and the diffusion problem for spatial transport of chemical signals, coupled via diffusiophoresis. A parametric study is performed with respect to crucial model parameters that control the diffusivity of the particles and the chemical signals. This includes the initial concentration of chemical signals carried by the particles, the chemical signal release rate, the diffusion coefficient of the chemical signals, the diffusiophoretic mobility of the particles, and the topological complexity of the surrounding environment. Three measures are used to evaluate the performance of the system: the target arrival time, the target localization success rate, and the target residence time. Since the particle motion is determined by the local concentration gradients of the chemical signals, parameter values that result in steep and durable concentration gradients lead to the best performance in navigating a swarm of colloidal particles toward the target. However, the results show that a trade-off principle exists, as it is not always possible to improve all the performance criteria simultaneously. For a topologically complex environment, the particles often become trapped in areas where the chemical signals accumulate, leading to a significant decrease in the localization success rate. PMID:23734621

  15. Velocity correlations in laboratory insect swarms

    NASA Astrophysics Data System (ADS)

    Ni, R.; Ouellette, N. T.

    2015-12-01

    In contrast to animal groups such as bird flocks or migratory herds that display net, directed motion, insect swarms do not possess global order. Without such order, it is difficult to define and characterize the transition to collective behavior in swarms; nevertheless, visual observation of swarms strongly suggests that swarming insects do behave collectively. It has recently been suggested that correlation rather than order is the hallmark of emergent collective behavior. Here, we report measurements of spatial velocity correlation functions in laboratory mating swarms of the non-biting midge Chironomus riparius. Although we find some correlation at short distances, our swarms are in general only weakly correlated, in contrast to what has been observed in field studies. Our results hint at the potentially important role of environmental conditions on collective behavior, and suggest that general indicators of the collective nature of swarming are still needed.

  16. A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

    PubMed

    Kentzoglanakis, Kyriakos; Poole, Matthew

    2012-01-01

    In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures. PMID:21576756

  17. Acoustic network event classification using swarm optimization

    NASA Astrophysics Data System (ADS)

    Burman, Jerry

    2013-05-01

    Classifying acoustic signals detected by distributed sensor networks is a difficult problem due to the wide variations that can occur in the transmission of terrestrial, subterranean, seismic and aerial events. An acoustic event classifier was developed that uses particle swarm optimization to perform a flexible time correlation of a sensed acoustic signature to reference data. In order to mitigate the effects from interference such as multipath, the classifier fuses signatures from multiple sensors to form a composite sensed acoustic signature and then automatically matches the composite signature with reference data. The approach can classify all types of acoustic events but is particularly well suited to explosive events such as gun shots, mortar blasts and improvised explosive devices that produce an acoustic signature having a shock wave component that is aperiodic and non-linear. The classifier was applied to field data and yielded excellent results in terms of reconstructing degraded acoustic signatures from multiple sensors and in classifying disparate acoustic events.

  18. A scanning drift tube apparatus for spatiotemporal mapping of electron swarms

    NASA Astrophysics Data System (ADS)

    Korolov, I.; Vass, M.; Bastykova, N. Kh.; Donkó, Z.

    2016-06-01

    A "scanning" drift tube apparatus, capable of mapping of the spatiotemporal evolution of electron swarms, developing between two plane electrodes under the effect of a homogeneous electric field, is presented. The electron swarms are initiated by photoelectron pulses and the temporal distributions of the electron flux are recorded while the electrode gap length (at a fixed electric field strength) is varied. Operation of the system is tested and verified with argon gas; the measured data are used for the evaluation of the electron bulk drift velocity. The experimental results for the space-time maps of the electron swarms — presented here for the first time — also allow clear observation of deviations from hydrodynamic transport. The swarm maps are also reproduced by particle simulations.

  19. Swarm intelligence algorithm for interconnect model order reduction with sub-block structure preserving

    NASA Astrophysics Data System (ADS)

    Wang, Xinsheng; Wang, Chenxu; Yu, Mingyan

    2016-07-01

    In this paper, we propose a generalised sub-block structure preservation interconnect model order reduction (MOR) technique based on the swarm intelligence method, that is, particle swarm optimisation (PSO). The swarm intelligence-based structure preservation MOR can be used for a standard model as a criterion for different structure preservation interconnect MOR methods. In the proposed technique, the PSO method is used for predicting the unknown elements of structure-preserving reduced-order modelling of interconnect circuits. The prediction is based on minimising the difference of transform function between the original full-order and desired reduced-order systems maintaining the full-order structure in the reduced-order model. The proposed swarm-intelligence-based structure-preserving MOR method is compared with published work on structure preservation MOR SPRIM techniques. Simulation and synthesis results verify the accuracy and validity of the new structure-preserving MOR technique.

  20. A scanning drift tube apparatus for spatiotemporal mapping of electron swarms.

    PubMed

    Korolov, I; Vass, M; Bastykova, N Kh; Donkó, Z

    2016-06-01

    A "scanning" drift tube apparatus, capable of mapping of the spatiotemporal evolution of electron swarms, developing between two plane electrodes under the effect of a homogeneous electric field, is presented. The electron swarms are initiated by photoelectron pulses and the temporal distributions of the electron flux are recorded while the electrode gap length (at a fixed electric field strength) is varied. Operation of the system is tested and verified with argon gas; the measured data are used for the evaluation of the electron bulk drift velocity. The experimental results for the space-time maps of the electron swarms - presented here for the first time - also allow clear observation of deviations from hydrodynamic transport. The swarm maps are also reproduced by particle simulations. PMID:27370421