Sun, Jun; Fang, Wei; Wu, Xiaojun; Palade, Vasile; Xu, Wenbo
20120101
Quantumbehaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the barebones 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 contractionexpansion (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 wellknown 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.
Yang, ZhenLun; Wu, Angus; Min, HuaQing
20150101
An improved quantumbehaved particle swarm optimization with elitist breeding (EBQPSO) for unconstrained optimization is presented and empirically studied in this paper. In EBQPSO, 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 EBQPSO, 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 stateoftheart quantumbehaved particle swarm optimization algorithms, the proposed EBQPSO performs more competitively in all of the benchmark functions in terms of better global search capability and faster convergence rate. PMID:26064085
Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng
20150101
Parameter estimation for fractionalorder chaotic systems is an important issue in fractionalorder 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 fractionalorder 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 fractionalorder systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm. PMID:25603158
QPSOMD: A Quantum Behaved Particle Swarm Optimization for Consensus Pattern Identification
NASA Astrophysics Data System (ADS)
Meshoul, Souham; AlOwaisheq, Tasneem
Particle Swarm Optimization (PSO) has been successfully applied to a wide range of fields. The recent introduction of quantum mechanics principles into PSO has given rise to a Quantum behaviour PSO (QPSO) algorithm. This paper investigates its application into motif discovery, a challenging task in bioinformatics and molecular biology. Given a set of input DNA sequences, the proposed framework acts as a search process where a population of particles is depicted by a quantum behavior. Each particle represents a set of regulatory patterns from which a consensus pattern or motif model is derived. The corresponding fitness function is related to the total number of pairwise matches between nucleotides in the input sequences. Experiment results on synthetic and real data are very promising and prove the effectiveness of the proposed framework.
NASA Technical Reports Server (NTRS)
Venter, Gerhard; SobieszczanskiSobieski Jaroslaw
20020101
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.
Particle Swarm Optimization Toolbox
NASA Technical Reports Server (NTRS)
Grant, Michael J.
20100101
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 singleobjective particle swarm optimizer (SOPSO), and a multiobjective 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 multiobjective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and birdflocking 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 usersupplied objective function. This function serves as a "blackbox" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the usersupplied 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
Li, Desheng
20140101
This paper proposes a novel variant of cooperative quantumbehaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSODVSALFD. 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 CQPSODVSALFD, 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 jobshop scheduling problem. PMID:24851085
NASA Astrophysics Data System (ADS)
Zhao, Jianhu; Wang, Xiao; Zhang, Hongmei; Hu, Jun; Jian, Xiaomin
20160601
To fulfill side scan sonar (SSS) image segmentation accurately and efficiently, a novel segmentation algorithm based on neutrosophic set (NS) and quantumbehaved 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 cooccurrence 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 twodimensional 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.
NASA Astrophysics Data System (ADS)
Zhao, Jianhu; Wang, Xiao; Zhang, Hongmei; Hu, Jun; Jian, Xiaomin
20160901
To fulfill side scan sonar (SSS) image segmentation accurately and efficiently, a novel segmentation algorithm based on neutrosophic set (NS) and quantumbehaved 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 cooccurrence 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 twodimensional 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.
Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun
20160101
This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantumbehaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 01 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leaveoneout cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms. PMID:27642363
Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun
20160101
This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantumbehaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 01 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leaveoneout cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.
Xi, Maolong; Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun
20160101
This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantumbehaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 01 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leaveoneout cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms. PMID:27642363
Particle Swarm Optimization with Double Learning Patterns.
Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian
20160101
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 (PSODLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a tradeoff 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 PSODLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSODLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747
Particle Swarm Optimization with Double Learning Patterns
Shen, Yuanxia; Wei, Linna; Zeng, Chuanhua; Chen, Jian
20160101
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 (PSODLP) is developed, which employs the master swarm and the slave swarm with different learning patterns to achieve a tradeoff 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 PSODLP is evaluated on 20 benchmark functions, including rotated multimodal and complex shifted problems. The simulation results and statistical analysis show that PSODLP obtains a promising performance and outperforms eight PSO variants. PMID:26858747
Particle Swarm Transport in Fracture Networks
NASA Astrophysics Data System (ADS)
PyrakNolte, L. J.; Mackin, T.; Boomsma, E.
20121201
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 & microparticles 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 crosssectional 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 (3060 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
Resolving superimposed MUAPs using particle swarm optimization.
Marateb, Hamid Reza; McGill, Kevin C
20090301
This paper presents an algorithm to resolve superimposed action potentials encountered during the decomposition of electromyographic signals. The algorithm uses particle swarm optimization with a variety of features including randomization, crossover, and multiple swarms. In a simulation study involving realistic superpositions of two to five motorunit action potentials, the algorithm had an accuracy of 98%.
Transport of Particle Swarms Through Fractures
NASA Astrophysics Data System (ADS)
Boomsma, E.; PyrakNolte, L. J.
20111201
The transport of engineered micro and nanoscale 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 colloidalsize 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 (funnelshaped). 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 sodalime 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
Selectivelyinformed particle swarm optimization.
Gao, Yang; Du, Wenbo; Yan, Gang
20150101
Particle swarm optimization (PSO) is a natureinspired 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 selectivelyinformed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a denselyconnected hub particle gets full information from all of its neighbors while a nonhub particle with few connections can only follow a single yet bestperformed neighbor. Extensive numerical experiments on widelyused 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 nonhub 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
Selectivelyinformed particle swarm optimization
Gao, Yang; Du, Wenbo; Yan, Gang
20150101
Particle swarm optimization (PSO) is a natureinspired 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 selectivelyinformed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a denselyconnected hub particle gets full information from all of its neighbors while a nonhub particle with few connections can only follow a single yet bestperformed neighbor. Extensive numerical experiments on widelyused 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 nonhub 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
Selectivelyinformed particle swarm optimization
NASA Astrophysics Data System (ADS)
Gao, Yang; Du, Wenbo; Yan, Gang
20150301
Particle swarm optimization (PSO) is a natureinspired 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 selectivelyinformed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a denselyconnected hub particle gets full information from all of its neighbors while a nonhub particle with few connections can only follow a single yet bestperformed neighbor. Extensive numerical experiments on widelyused 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 nonhub 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.
Particle Swarm Optimization with Dynamic Step Length
NASA Astrophysics Data System (ADS)
Cui, Zhihua; Cai, Xingjuan; Zeng, Jianchao; Sun, Guoji
Particle swarm optimization (PSO) is a robust swarm intelligent technique inspired from birds flocking and fish schooling. Though many effective improvements have been proposed, however, the premature convergence is still its main problem. Because each particle's movement is a continuous process and can be modelled with differential equation groups, a new variant, particle swarm optimization with dynamic step length (PSODSL), with additional control coefficient step length, is introduced. Then the absolute stability theory is introduced to analyze the stability character of the standard PSO, the theoretical result indicates the PSO with constant step length can not always be stable, this may be one of the reason for premature convergence. Simulation results show the PSODSL is effective.
Incremental social learning in particle swarms.
de Oca, Marco A Montes; Stutzle, Thomas; Van den Enden, Ken; Dorigo, Marco
20110401
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 populationbased 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 bestsofar 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
Emergent system identification using particle swarm optimization
NASA Astrophysics Data System (ADS)
Voss, Mark S.; Feng, Xin
20011001
Complex Adaptive Structures can be viewed as a combination of Complex Adaptive Systems and fully integrated autonomous Smart Structures. Traditionally when designing a structure, one combines rules of thumb with theoretical results to develop an acceptable solution. This methodology will have to be extended for Complex Adaptive Structures, since they, by definition, will participate in their own design. In this paper we introduce a new methodology for Emergent System Identification that is concerned with combining the methodologies of selforganizing functional networks (GMDH  Alexy G. Ivakhnenko), Particle Swarm Optimization (PSO  James Kennedy and Russell C. Eberhart) and Genetic Programming (GP  John Koza). This paper will concentrate on the utilization of Particle Swarm Optimization in this effort and discuss how Particle Swarm Optimization relates to our ultimate goal of emergent selforganizing functional networks that can be used to identify overlapping internal structural models. The ability for Complex Adaptive Structures to identify emerging internal models will be a key component for their success.
An Improved Particle Swarm Optimization for Traveling Salesman Problem
NASA Astrophysics Data System (ADS)
Liu, Xinmei; Su, Jinrong; Han, Yan
In allusion to particle swarm optimization being prone to get into local minimum, an improved particle swarm optimization algorithm is proposed. The algorithm draws on the thinking of the greedy algorithm to initialize the particle swarm. Two swarms are used to optimize synchronously. Crossover and mutation operators in genetic algorithm are introduced into the new algorithm to realize the sharing of information among swarms. We test the algorithm with Traveling Salesman Problem with 14 nodes and 30 nodes. The result shows that the algorithm can break away from local minimum earlier and it has high convergence speed and convergence ratio.
Lagrange Interpolation Learning Particle Swarm Optimization.
Kai, Zhang; Jinchun, Song; Ke, Ni; Song, Li
20160101
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
Multiswarm Particle Swarm Optimization with Transfer of the Best Particle
Wei, Xiaopeng; Zhang, Jianxia; Zhou, Dongsheng; Zhang, Qiang
20150101
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
Lagrange Interpolation Learning Particle Swarm Optimization
20160101
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
Cosmological parameter estimation using Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Prasad, J.; Souradeep, T.
20140301
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.
Chaotic Particle Swarm Optimization with Mutation for Classification
Assarzadeh, Zahra; NaghshNilchi, Ahmad Reza
20150101
In this paper, a chaotic particle swarm optimization with mutationbased 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, mutationbased classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heartstatlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including knearest neighbor, as a conventional classifier, particle swarmclassifier, genetic algorithm, and Imperialist competitive algorithmclassifier, 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 mutationbased classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937
Chaotic particle swarm optimization with mutation for classification.
Assarzadeh, Zahra; NaghshNilchi, Ahmad Reza
20150101
In this paper, a chaotic particle swarm optimization with mutationbased 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, mutationbased classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heartstatlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including knearest neighbor, as a conventional classifier, particle swarmclassifier, genetic algorithm, and Imperialist competitive algorithmclassifier, 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 mutationbased classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937
Transport of Particle Swarms Through Variable Aperture Fractures
NASA Astrophysics Data System (ADS)
Boomsma, E.; PyrakNolte, L. J.
20121201
Particle transport through fractured rock is a key concern with the increased use of micro and nanosize 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 droplike collections of millions of colloidalsized 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
Human behaviorbased particle swarm optimization.
Liu, Hao; Xu, Gang; Ding, GuiYan; Sun, YuBo
20140101
Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behaviorbased PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients c 1 and c 2 in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted highdimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost.
Computer Visualization of ManyParticle Quantum Dynamics
Ozhigov, A. Y.
20090310
In this paper I show the importance of computer visualization in researching of manyparticle quantum dynamics. Such a visualization becomes an indispensable illustrative tool for understanding the behavior of dynamic swarmbased quantum systems. It is also an important component of the corresponding simulation framework, and can simplify the studies of underlying algorithms for multiparticle quantum systems.
Computer Visualization of ManyParticle Quantum Dynamics
NASA Astrophysics Data System (ADS)
Ozhigov, A. Y.
20090301
In this paper I show the importance of computer visualization in researching of manyparticle quantum dynamics. Such a visualization becomes an indispensable illustrative tool for understanding the behavior of dynamic swarmbased quantum systems. It is also an important component of the corresponding simulation framework, and can simplify the studies of underlying algorithms for multiparticle quantum systems.
Particle Swarm Transport through Immiscible Fluid Layers in a Fracture
NASA Astrophysics Data System (ADS)
Teasdale, N. D.; Boomsma, E.; PyrakNolte, L. J.
20111201
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 nanoscale 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 colloidalsize 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 breakup of colloidal swarms under gravity in a uniform aperture fracture as hydrophobic/hydrophyllic particle swarms move across an oilwater 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 oilwater interface, it
Particle Swarms in Fractures: Open Versus Partially Closed Systems
NASA Astrophysics Data System (ADS)
Boomsma, E.; PyrakNolte, L. J.
20141201
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 colloidalsized 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 580 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 sodalime 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 13. The nonrigid 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
Searching for Planets using Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Chambers, John E.
20080501
The Doppler radial velocity technique has been highly successful in discovering planetarymass companions in orbit around nearby stars. A typical data set contains around one hundred instantaneous velocities for the star, spread over a period of several years,with each observation measuring only the radial component of velocity. From this data set, one would like to determine the masses and orbital parameters of the system of planets responsible for the star's reflex motion. Assuming coplanar orbits, each planet is characterized by five parameters, with an additional parameter for each telescope used to make observations, representing the instrument's velocity offset. The large number of free parameters and the relatively sparse data sets make the fitting process challenging when multiple planets are present, especially if some of these objects have low masses. Conventional approaches using periodograms often perform poorly when the orbital periods are not separated by large amounts or the longest period is comparable to the length of the data set. Here, I will describe a new approach to fitting Doppler radial velocity sets using particle swarm optimization (PSO). I will describe how the PSO method works, and show examples of PSO fits to existing radial velocity data sets, with comparisons to published solutions and those submitted to the Systemic website (http://www.oklo.org).
A Novel Particle Swarm Optimization Algorithm for Global Optimization
Wang, ChunFeng; Liu, Kui
20160101
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
Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah
20150101
The highdimensional search space involved in markerless fullbody articulated human motion tracking from multipleviews 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 MultiSwarm Cooperative Particle Swarm Optimization (HMCPSO). The tracking problem is formulated as a nonlinear 34dimensional 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 HumanEvaII dataset demonstrated that HMCPSO performance is better than two leading alternative approachesAnnealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO). Further, the proposed tracking method is capable of automatic initialization and selfrecovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims.
Saini, Sanjay; Zakaria, Nordin; Rambli, Dayang Rohaya Awang; Sulaiman, Suziah
20150101
The highdimensional search space involved in markerless fullbody articulated human motion tracking from multipleviews 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 MultiSwarm Cooperative Particle Swarm Optimization (HMCPSO). The tracking problem is formulated as a nonlinear 34dimensional 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 HumanEvaII dataset demonstrated that HMCPSO 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 selfrecovery from temporary tracking failures. Comprehensive experimental results are presented to support the claims. PMID:25978493
Swarming of active colloidal Janus particles: Polar waves and vortices
NASA Astrophysics Data System (ADS)
Xu, Cong; Yan, Jing; Han, Ming; Luijten, Erik; Granick, Steve
20150301
The synthesis of artificial ``swarming'' particles with tunable interaction represents a strong interest of the soft active matter community. Here, we demonstrate a straightforward design of swarming Janus colloids that exhibit transient mutual alignment within a certain frequency range of an applied AC electric field. In a dense twodimensional suspension of these Janus colloids, we observe that coherent polar waves emerge at first, which then collide and merge into stable discrete vortices. Based upon a careful analysis of the pair interaction, we propose a simple mechanism that explains the formation of the polar waves, with agreement between experiment and simulation. A rich spectrum of phenomena, including dimer swarming, chain formation, and particle clustering, can be further achieved by changing the frequency of the AC electric field. Currently working as a postdoctoral researcher in Princeton University.
An improved particle swarm optimization algorithm for reliability problems.
Wu, Peifeng; Gao, Liqun; Zou, Dexuan; Li, Steven
20110101
An improved particle swarm optimization (IPSO) algorithm is proposed to solve reliability problems in this paper. The IPSO designs two position updating strategies: In the early iterations, each particle flies and searches according to its own best experience with a large probability; in the late iterations, each particle flies and searches according to the fling experience of the most successful particle with a large probability. In addition, the IPSO introduces a mutation operator after position updating, which can not only prevent the IPSO from trapping into the local optimum, but also enhances its space developing ability. Experimental results show that the proposed algorithm has stronger convergence and stability than the other four particle swarm optimization algorithms on solving reliability problems, and that the solutions obtained by the IPSO are better than the previously reported bestknown solutions in the recent literature.
Particle swarm optimization applied to impulsive orbital transfers
NASA Astrophysics Data System (ADS)
Pontani, Mauro; Conway, Bruce A.
20120501
The particle swarm optimization (PSO) technique is a populationbased stochastic method developed in recent years and successfully applied in several fields of research. It mimics the unpredictable motion of bird flocks while searching for food, with the intent of determining the optimal values of the unknown parameters of the problem under consideration. At the end of the process, the best particle (i.e. the best solution with reference to the objective function) is expected to contain the globally optimal values of the unknown parameters. The central idea underlying the method is contained in the formula for velocity updating. This formula includes three terms with stochastic weights. This research applies the particle swarm optimization algorithm to the problem of optimizing impulsive orbital transfers. More specifically, the following problems are considered and solved with the PSO algorithm: (i) determination of the globally optimal two and threeimpulse transfer trajectories between two coplanar circular orbits; (ii) determination of the optimal transfer between two coplanar, elliptic orbits with arbitrary orientation; (iii) determination of the optimal twoimpulse transfer between two circular, noncoplanar orbits; (iv) determination of the globally optimal twoimpulse transfer between two noncoplanar elliptic orbits. Despite its intuitiveness and simplicity, the particle swarm optimization method proves to be capable of effectively solving the orbital transfer problems of interest with great numerical accuracy.
Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization
Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali
20140101
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 (AAPSOSVM). 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 AAPSOSVM and the PSOSVM technique is presented. PMID:24790584
Support vector machine based on adaptive acceleration particle swarm optimization.
Abdulameer, Mohammed Hasan; Sheikh Abdullah, Siti Norul Huda; Othman, Zulaiha Ali
20140101
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 (AAPSOSVM). 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 AAPSOSVM and the PSOSVM technique is presented. PMID:24790584
Acoustic Radiation Optimization Using the Particle Swarm Optimization Algorithm
NASA Astrophysics Data System (ADS)
Jeon, JinYoung; Okuma, Masaaki
The present paper describes a fundamental study on structural bending design to reduce noise using a new evolutionary populationbased 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.
Particle Swarm Based Collective Searching Model for Adaptive Environment
Cui, Xiaohui; Patton, Robert M; Potok, Thomas E; Treadwell, Jim N
20080101
This report presents a pilot study of an integration of particle swarm algorithm, social knowledge adaptation and multiagent approaches for modeling the collective search behavior of selforganized 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 selforganized groups to achieve the efficient collective searching behavior in the adaptive environment.
Particle Swarm Based Collective Searching Model for Adaptive Environment
Cui, Xiaohui; Patton, Robert M; Potok, Thomas E; Treadwell, Jim N
20070101
This report presents a pilot study of an integration of particle swarm algorithm, social knowledge adaptation and multiagent approaches for modeling the collective search behavior of selforganized 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 selforganized groups to achieve the efficient collective searching behavior in the adaptive environment.
Roundness error assessment based on particle swarm optimization
NASA Astrophysics Data System (ADS)
Zhao, J. W.; Chen, G. Q.
20050101
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 ndimensional 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.
Particle swarm optimization for complex nonlinear optimization problems
NASA Astrophysics Data System (ADS)
Alexandridis, Alex; Famelis, Ioannis Th.; Tsitouras, Charalambos
20160601
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 RungeKutta 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.
Particle swarm optimization with recombination and dynamic linkage discovery.
Chen, YingPing; Peng, WenChih; Jian, MingChung
20071201
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 realparameter 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 (PSORDL). In order to study the capability of PSORDL, numerical experiments were conducted on a set of benchmark functions as well as on an important realworld 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 PSORDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSORDL was also used to solve the economic dispatch (ED) problem for power systems, which is a realworld problem and highly constrained. The results indicate that PSORDL can successfully solve the ED problem for the threeunit power system and obtain the currently known best solution for the 40unit system. PMID:18179066
Genetic algorithm and particle swarm optimization combined with Powell method
NASA Astrophysics Data System (ADS)
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
20131001
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 bioinspired 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.
Particle Swarm Imaging (PSIM)  Innovative GammaRay Assay  13497
Parvin, Daniel; Clarke, Sean; Humes, Sarah J.
20130701
Particle Swarm Imaging is an innovative technique used to perform quantitative gammaray 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 gammaray detector. Examples of its application in the field are given in this paper. (authors)
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.
Garro, Beatriz A; Vázquez, Roberto A
20150101
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 wellknown BackPropagation and LevenbergMarquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132
Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms
Garro, Beatriz A.; Vázquez, Roberto A.
20150101
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 wellknown BackPropagation and LevenbergMarquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132
A Parallel Particle Swarm Optimization Algorithm Accelerated by Asynchronous Evaluations
NASA Technical Reports Server (NTRS)
Venter, Gerhard; SobieszczanskiSobieski, Jaroslaw
20050101
A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of nongradient 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 coarsegrained 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 multidisciplinary optimization of a typical transport aircraft wing as an example.
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 populationbased 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 nearoptimal 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.
Particle swarm optimization of ascent trajectories of multistage launch vehicles
NASA Astrophysics Data System (ADS)
Pontani, Mauro
20140201
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 populationbased 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 threedimensional 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 EulerLagrange equations and the Pontryagin minimum principle, in conjunction with the WeierstrassErdmann 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
ERIC Educational Resources Information Center
Petersen, Hugh
20020101
Describes an eighth grade art project for which students created bug swarms on scratchboard. Explains that the project also teaches students about design principles, such as balance. Discusses how the students created their drawings. (CMK)
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.
20030701
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 NPhard problem. Solutions to NPhard problems involve searches through vast spaces of possible solutions. Evolutionary algorithms have been applied successfully to a variety of NPhard 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 dataaggregation type sensor network deployment is tested using a modified LEACHC 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.
Particle Swarm and Ant Colony Approaches in Multiobjective Optimization
NASA Astrophysics Data System (ADS)
Rao, S. S.
20101001
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 Paretooptimal 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.
Transmitter antenna placement in indoor environments using particle swarm optimisation
NASA Astrophysics Data System (ADS)
Talepour, Zeinab; Tavakoli, Saeed; AhmadiShokouh, Javad
20130701
The aim of this article is to suitably locate the minimum number of transmitter antennas in a given indoor environment to achieve good propagation coverage. To calculate the electromagnetic field in various points of the environment, we develop a software engine, named raytracing engine (RTE), in Matlab. To achieve realistic calculations, all parameters of geometry and material of building are considered. Particle swarm optimisation is employed to determine good location of transmitters. Simulation results show that a full coverage is obtained through suitably locating three transmitters.
What is Particle Swarm optimization? Application to hydrogeophysics (Invited)
NASA Astrophysics Data System (ADS)
Fernández Martïnez, J.; García Gonzalo, E.; Mukerji, T.
20091201
Inverse problems are generally illposed. 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 wellknown 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 springmass 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 lowcost 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 ColeCole 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
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 nonlinear nature of the engine performance problem. In this thesis, a Particle Swarm Optimization (PSO) variant was applied to maximize the specific impulse of a finitearea combustion chamber (FAC) equilibrium flow rocket performance model by controlling the engine's oxidizertofuel ratio and de Laval nozzle expansion and contraction ratios. In addition to the PSOcontrolled 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 bruteforce 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 nonlinear variables.
Parallel global optimization with the particle swarm algorithm.
Schutte, J F; Reinbolt, J A; Fregly, B J; Haftka, R T; George, A D
20041201
Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern highend processor. To obtain enhanced computational throughput and global search capability, we detail the coarsegrained 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 minimalargescale analytical test problems with computationally cheap function evaluations and mediumscale biomechanical system identification problems with computationally expensive function evaluations. For loadbalanced 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 loadimbalanced 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 subpopulations (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 loadbalanced conditions, (2) an asynchronous implementation would be valuable for reallife problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available.
Parallel global optimization with the particle swarm algorithm
Schutte, J. F.; Reinbolt, J. A.; Fregly, B. J.; Haftka, R. T.; George, A. D.
20070101
SUMMARY Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern highend processor. To obtain enhanced computational throughput and global search capability, we detail the coarsegrained 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—largescale analytical test problems with computationally cheap function evaluations and mediumscale biomechanical system identification problems with computationally expensive function evaluations. For loadbalanced 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 loadimbalanced 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 subpopulations (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 loadbalanced conditions, (2) an asynchronous implementation would be valuable for reallife problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available. PMID:17891226
Microwavebased medical diagnosis using particle swarm optimization algorithm
NASA Astrophysics Data System (ADS)
Modiri, Arezoo
This dissertation proposes and investigates a novel architecture intended for microwavebased 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 Xray and gammaraybased 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 swarmintelligencebased 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
Particle Swarm Inspired Underwater Sensor SelfDeployment
Du, Huazheng; Xia, Na; Zheng, Rong
20140101
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 selfdeployment 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
Fishing for Data: Using Particle Swarm Optimization to Search Data
NASA Astrophysics Data System (ADS)
Caputo, Daniel P.; Dolan, R.
20100101
As the size of data and model sets continue to increase, more efficient ways are needed to sift through the available information. We present a computational method which will efficiently search large parameter spaces to either map the space or find individual data/models of interest. Particle swarm optimization (PSO) is a subclass of artificial life computer algorithms. The PSO algorithm attempts to leverage "swarm intelligence” against finding optimal solutions to a problem. This system is often based on a biological model of a swarm (e.g. schooling fish). These biological models are broken down into a few simple rules which govern the behavior of the system. "Agents” (e.g. fish) are introduced and the agents, following the rules, search out solutions much like a fish would seek out food. We have made extensive modifications to the standard PSO model which increase its efficiency aswellas adding the capacity to map a parameter space and find multiple solutions. Our modified PSO is ideally suited to search and map large sets of data/models which are degenerate or to search through data/models which are too numerous to analyze by hand. One example of this would include radiative transfer models, which are inherently degenerate. Applying the PSO algorithm will allow the degeneracy space to be mapped and thus better determine limits on dust shell parameters. Another example is searching through legacy data from a survey for hints of Polycyclic Aromatic Hydrocarbon emission. What might have once taken years of searching (and many frustrated graduate students) can now be relegated to the task of a computer which will work day and night for only the cost of electricity. We hope this algorithm will allow fellow astronomers to more efficiently search data and models, thereby freeing them to focus on the physics of the Universe.
A Triangle Mesh Standardization Method Based on Particle Swarm Optimization.
Wang, Wuli; Duan, Liming; Bai, Yang; Wang, Haoyu; Shao, Hui; Zhong, Siyang
20160101
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
Generalized Particle Swarm Algorithm for HCR Gearing Geometry Optimization
NASA Astrophysics Data System (ADS)
Kuzmanović, Siniša; Vereš, Miroslav; Rackov, Milan
20121201
R2Based Multi/ManyObjective Particle Swarm Optimization
Toscano, Gregorio; BarronZambrano, Jose Hugo; TelloLeal, Edgar
20160101
We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/manyobjective problems. Our proposal shows that through a welldesigned interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed algorithm produces results that are competitive with respect to those obtained by four wellknown MOEAs. Additionally, we validate our proposal in manyobjective optimization problems. In these problems, our approach showed its main strength, since it could outperform another wellknown indicatorbased MOEA.
Particle Swarm Optimization Approach in a Consignment Inventory System
NASA Astrophysics Data System (ADS)
Sharifyazdi, Mehdi; Jafari, Azizollah; Molamohamadi, Zohreh; Rezaeiahari, Mandana; Arshizadeh, Rahman
20090901
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.
Planar straightness error evaluation based on particle swarm optimization
NASA Astrophysics Data System (ADS)
Mao, Jian; Zheng, Huawen; Cao, Yanlong; Yang, Jiangxin
20061101
The straightness error generally refers to the deviation between an actual line and an ideal line. According to the characteristics of planar straightness error evaluation, a novel method to evaluate planar straightness errors based on the particle swarm optimization (PSO) is proposed. The planar straightness error evaluation problem is formulated as a nonlinear optimization problem. According to minimum zone condition the mathematical model of planar straightness together with the optimal objective function and fitness function is developed. Compared with the genetic algorithm (GA), the PSO algorithm has some advantages. It is not only implemented without crossover and mutation but also has fast congruence speed. Moreover fewer parameters are needed to set up. The results show that the PSO method is very suitable for nonlinear optimization problems and provides a promising new method for straightness error evaluation. It can be applied to deal with the measured data of planar straightness obtained by the threecoordinates measuring machines.
A Triangle Mesh Standardization Method Based on Particle Swarm Optimization
Duan, Liming; Bai, Yang; Wang, Haoyu; Shao, Hui; Zhong, Siyang
20160101
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
R2Based Multi/ManyObjective Particle Swarm Optimization
Toscano, Gregorio; BarronZambrano, Jose Hugo; TelloLeal, Edgar
20160101
We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/manyobjective problems. Our proposal shows that through a welldesigned interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approach is validated using several test problems and performance measures commonly adopted in the specialized literature. Results indicate that the proposed algorithm produces results that are competitive with respect to those obtained by four wellknown MOEAs. Additionally, we validate our proposal in manyobjective optimization problems. In these problems, our approach showed its main strength, since it could outperform another wellknown indicatorbased MOEA. PMID:27656200
A Bayesian interpretation of the particle swarm optimization and its kernel extension.
Andras, Peter
20120101
Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we present a Bayesian interpretation of the particle swarm optimization. This interpretation provides a formal framework for incorporation of prior knowledge about the problem that is being solved. Furthermore, it also allows to extend the particle optimization method through the use of kernel functions that represent the intermediary transformation of the data into a different space where the optimization problem is expected to be easier to be resolvedsuch transformation can be seen as a form of prior knowledge about the nature of the optimization problem. We derive from the general Bayesian formulation the commonly used particle swarm methods as particular cases.
A Bayesian Interpretation of the Particle Swarm Optimization and Its Kernel Extension
Andras, Peter
20120101
Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we present a Bayesian interpretation of the particle swarm optimization. This interpretation provides a formal framework for incorporation of prior knowledge about the problem that is being solved. Furthermore, it also allows to extend the particle optimization method through the use of kernel functions that represent the intermediary transformation of the data into a different space where the optimization problem is expected to be easier to be resolved–such transformation can be seen as a form of prior knowledge about the nature of the optimization problem. We derive from the general Bayesian formulation the commonly used particle swarm methods as particular cases. PMID:23144937
The infrared spectral transmittance of Aspergillus niger spore aggregated particle swarm
NASA Astrophysics Data System (ADS)
Zhao, Xinying; Hu, Yihua; Gu, Youlin; Li, Le
20151001
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 clustercluster 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
A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.
Amoshahy, Mohammad Javad; Shamsi, Mousa; Sedaaghi, Mohammad Hossein
20160101
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 timevarying, 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
Expedite Particle Swarm Optimization Algorithm (EPSO) for Optimization of MSA
NASA Astrophysics Data System (ADS)
Rathi, Amit; Vijay, Ritu
This paper presents a new designing method of Rectangular patch Microstrip Antenna using an Artificial searches Algorithm with some constraints. It requires two stages for designing. In first stage, bandwidth of MSA is modeled using bench Mark function. In second stage, output of first stage give to modified Artificial search Algorithm which is Particle Swarm Algorithm (PSO) as input and get output in the form of five parameter dimensions width, frequency range, dielectric loss tangent, length over a ground plane with a substrate thickness and electrical thickness. In PSO Cognition, factor and Social learning Factor give very important effect on balancing the local search and global search in PSO. Basing the modification of cognition factor and social learning factor, this paper presents the strategy that at the starting process cognitionlearning factor has more effect then social learning factor. Gradually social learning factor has more impact after learning cognition factor for find out global best. The aim is to find out under above circumstances these modifications in PSO can give better result for optimization of microstrip Antenna (MSA).
A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm
Shamsi, Mousa; Sedaaghi, Mohammad Hossein
20160101
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 timevarying, 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
A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.
Amoshahy, Mohammad Javad; Shamsi, Mousa; Sedaaghi, Mohammad Hossein
20160101
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 timevarying, 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.
Application of Particle Swarm Optimization in Computer Aided Setup Planning
NASA Astrophysics Data System (ADS)
Kafashi, Sajad; Shakeri, Mohsen; Abedini, Vahid
20110101
New researches are trying to integrate computer aided design (CAD) and computer aided manufacturing (CAM) environments. The role of process planning is to convert the design specification into manufacturing instructions. Setup planning has a basic role in computer aided process planning (CAPP) and significantly affects the overall cost and quality of machined part. This research focuses on the development for automatic generation of setups and finding the best setup plan in feasible condition. In order to computerize the setup planning process, three major steps are performed in the proposed system: a) Extraction of machining data of the part. b) Analyzing and generation of all possible setups c) Optimization to reach the best setup plan based on cost functions. Considering workshop resources such as machine tool, cutter and fixture, all feasible setups could be generated. Then the problem is adopted with technological constraints such as TAD (tool approach direction), tolerance relationship and feature precedence relationship to have a completely real and practical approach. The optimal setup plan is the result of applying the PSO (particle swarm optimization) algorithm into the system using cost functions. A real sample part is illustrated to demonstrate the performance and productivity of the system.
Particle Swarm Optimization with ScaleFree Interactions
Liu, Chen; Du, WenBo; Wang, WenXu
20140101
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 scalefree network to represent the interindividual interactions in the population, named SFPSO. In contrast to the traditional PSO with fullyconnected topology or regular topology, the scalefree topology used in SFPSO 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 SFPSO 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 nonhub nodes play a crucial role in optimizing the convergence process. Our work may have implications in computational intelligence and complex networks. PMID:24859007
Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Kiranyaz, Serkan; Uhlmann (Eurasip Member), Stefan; Ince, Turker; Gabbouj, Moncef
20101201
Color is the major source of information widely used in image analysis and contentbased retrieval. Extracting dominant colors that are prominent in a visual scenery is of utmost importance since the human visual system primarily uses them for perception and similarity judgment. In this paper, we address dominant color extraction as a dynamic clustering problem and use techniques based on Particle Swarm Optimization (PSO) for finding optimal (number of) dominant colors in a given color space, distance metric and a proper validity index function. The first technique, socalled Multidimensional (MD) PSO can seek both positional and dimensional optima. Nevertheless, MD PSO is still susceptible to premature convergence due to lack of divergence. To address this problem we then apply Fractional Global Best Formation (FGBF) technique. In order to extract perceptually important colors and to further improve the discrimination factor for a better clustering performance, an efficient color distance metric, which uses a fuzzy model for computing color (dis) similarities over HSV (or HSL) color space is proposed. The comparative evaluations against MPEG7 dominant color descriptor show the superiority of the proposed technique.
Delay induced instabilities in selfpropelling swarming particles
NASA Astrophysics Data System (ADS)
Forgoston, Eric; Schwartz, Ira
20080301
We consider a general model of selfpropelling biological or artificial individuals interacting through a pairwise attractive force in a twodimensional 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.
NASA Astrophysics Data System (ADS)
Huang, Haibin; Zhuang, Yufei
20150801
This paper proposes a method that plans energyoptimal trajectories for multisatellite formation reconfiguration in deep space environment. A novel coevolutionary 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 coevolutionary particle swarm optimization method, with which the computation time can be shorten a lot. In order to make the actual trajectories optimal and collisionfree with disturbance, a replanning strategy is deduced for formation reconfiguration maneuver.
Discrete particle swarm optimization with scout particles for library materials acquisition.
Wu, YiLing; Ho, TsuFeng; Shyu, Shyong Jian; Lin, Bertrand M T
20130101
Materials acquisition is one of the critical challenges faced by academic libraries. This paper presents an integer programming model of the studied problem by considering how to select materials in order to maximize the average preference and the budget execution rate under some practical restrictions including departmental budget, limitation of the number of materials in each category and each language. To tackle the constrained problem, we propose a discrete particle swarm optimization (DPSO) with scout particles, where each particle, represented as a binary matrix, corresponds to a candidate solution to the problem. An initialization algorithm and a penalty function are designed to cope with the constraints, and the scout particles are employed to enhance the exploration within the solution space. To demonstrate the effectiveness and efficiency of the proposed DPSO, a series of computational experiments are designed and conducted. The results are statistically analyzed, and it is evinced that the proposed DPSO is an effective approach for the studied problem.
Discrete particle swarm optimization with scout particles for library materials acquisition.
Wu, YiLing; Ho, TsuFeng; Shyu, Shyong Jian; Lin, Bertrand M T
20130101
Materials acquisition is one of the critical challenges faced by academic libraries. This paper presents an integer programming model of the studied problem by considering how to select materials in order to maximize the average preference and the budget execution rate under some practical restrictions including departmental budget, limitation of the number of materials in each category and each language. To tackle the constrained problem, we propose a discrete particle swarm optimization (DPSO) with scout particles, where each particle, represented as a binary matrix, corresponds to a candidate solution to the problem. An initialization algorithm and a penalty function are designed to cope with the constraints, and the scout particles are employed to enhance the exploration within the solution space. To demonstrate the effectiveness and efficiency of the proposed DPSO, a series of computational experiments are designed and conducted. The results are statistically analyzed, and it is evinced that the proposed DPSO is an effective approach for the studied problem. PMID:24072983
Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization
NASA Technical Reports Server (NTRS)
Birge, Brian
20130101
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 timecritical 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, Earthbased 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 retasked at will and run in realtime 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 nonstationary error topologies as well.
Evaluation of a Particle Swarm Algorithm For Biomechanical Optimization
Schutte, Jaco F.; Koh, Byung; Reinbolt, Jeffrey A.; Haftka, Raphael T.; George, Alan D.; Fregly, Benjamin J.
20060101
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 gradientbased algorithms can be affected by scaling to account for design variables with different length scales or units. In this study we evaluate a recentlydeveloped 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 scaleindependent nature was proven mathematically and verified using a biomechanical test problem. For comparison, all test problems were also solved with three offtheshelf optimization algorithms—a global genetic algorithm (GA) and multistart gradientbased sequential quadratic programming (SQP) and quasiNewton (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 offtheshelf global optimization option for difficult biomechanical problems, especially those utilizing design variables with different length scales or units. PMID:16060353
Particle swarm optimization and uncertainty in DempsterShafer fusion
NASA Astrophysics Data System (ADS)
Ranney, Kenneth; Nasrabadi, Nasser
20090501
Recent investigations into multisensor fusion have yielded a variety of data fusion algorithms. Some fuse imagery from multiple sensors at the pixel level, while others fuse outputs of detection algorithmssuch as radar prescreeners or hyperspectral anomaly detectorsat the feature level. Many of the featurelevel fusion algorithms build upon the foundation of Bayesian probability, and they assign probability to the event that a certain feature value is due either to a target or to clutter. A few of the featurelevel fusion algorithms, however, exploit tools developed within the framework of the DempsterShafer (DS) theory of evidence. In these formulations some of the probability can be assigned to a third hypothesis representing uncertainty, and the algorithm developer must specify an uncertainty function that maps feature values to probability "mass" for this third hypothesis. Unfortunately, the DS paradigm lacks a standard method for assignment of mass to the "don't know" hypothesis for a particular input feature. In this paper we define a featurelevel DS fusion algorithm and determine a method for specifying its uncertainty function. We begin by developing and describing a measure of performance based on the area under the receiver operating characteristic (ROC) curve. We then incorporate this measure of performance into a training procedure that exploits the dynamics of the particle swarm and is capable of discovering locally optimal uncertainty functions. We exercise the training algorithm using simulated data, and analyze the performance of its hypothesized optimal uncertainty function. Next we apply the newly developed training techniques to data produced by separate prescreener algorithms operating on measured Hyperspectral Imager (HSI) and synthetic aperture radar (SAR) data from the same scene. Finally, we quantify the performance of the entire DS fusion procedure using ROC curves.
GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS
Rogers, Adam; Fiege, Jason D.
20110201
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 pointspread 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 'matrixfree' approach avoids construction of the lens and blurring operators while retaining the leastsquares 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 Lcurve for each solution automatically, which represents the tradeoff 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.
Purely hydrodynamic origin for swarming of swimming particles
NASA Astrophysics Data System (ADS)
Oyama, Norihiro; Molina, John Jairo; Yamamoto, Ryoichi
20160401
Threedimensional 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).
NASA Astrophysics Data System (ADS)
Lu, Shengtao; Liu, Fang; Xing, Bengang; Yeow, Edwin K. L.
20151201
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 timelapse 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 selfpropelled particle model that takes into account interparticle alignment and hardcore repulsion. This work has pertinent implications on the design of optimal methods to treat drug resistant bacteria commonly found in swarming colonies.
Lu, Shengtao; Liu, Fang; Xing, Bengang; Yeow, Edwin K L
20151201
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 timelapse 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 selfpropelled particle model that takes into account interparticle alignment and hardcore repulsion. This work has pertinent implications on the design of optimal methods to treat drug resistant bacteria commonly found in swarming colonies.
Application of Particle Swarm Optimization Algorithm in the Heating System Planning Problem
Ma, RongJiang; Yu, NanYang; Hu, JunYi
20130101
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
Particle swarmbased structural optimization of laminated composite hydrokinetic turbine blades
NASA Astrophysics Data System (ADS)
Li, H.; Chandrashekhara, K.
20150901
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 trialanderror method process, hydrokinetic turbine blade structural optimization using particle swarm optimization was proposed to perform detailed composite layup 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 outofplane loadcarrying 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.
Application of particle swarm optimization algorithm in the heating system planning problem.
Ma, RongJiang; Yu, NanYang; Hu, JunYi
20130101
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.
NASA Astrophysics Data System (ADS)
Arya, Rajesh; Purey, Pradeep
20160601
MWgeneration 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 MWgeneration 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.
Adaptive particle swarm optimization for optimal orbital elements of binary stars
NASA Astrophysics Data System (ADS)
Attia, AbdelFattah
20161001
The paper presents an adaptive particle swarm optimization (APSO) as an alternative method to determine the optimal orbital elements of the star η Bootis of MK type G0 IV. The proposed algorithm transforms the problem of finding periodic orbits into the problem of detecting global minimizers as a function, to get a best fit of Keplerian and Phase curves. The experimental results demonstrate that the proposed approach of APSO generally more accurate than the standard particle swarm optimization (PSO) and other published optimization algorithms, in terms of solution accuracy, convergence speed and algorithm reliability.
Particle Swarm Social Adaptive Model for MultiAgent Based Insurgency Warfare Simulation
Cui, Xiaohui; Potok, Thomas E
20091201
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 nonlinear 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.
Particle Swarm Social Model for Group Social Learning in Adaptive Environment
Cui, Xiaohui; Potok, Thomas E; Treadwell, Jim N; Patton, Robert M; Pullum, Laura L
20080101
This report presents a study of integrating particle swarm algorithm, social knowledge adaptation and multiagent approaches for modeling the social learning of selforganized 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 selforganized social groups, such as insurgents or online communities.
Zhang, Jianlei; Zhang, Chunyan; Chu, Tianguang; Perc, Matjaž
20110101
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
Coarsegrained variables for particlebased models: diffusion maps and animal swarming simulations
NASA Astrophysics Data System (ADS)
Liu, Ping; Safford, Hannah R.; Couzin, Iain D.; Kevrekidis, Ioannis G.
20141201
As microscopic (e.g. atomistic, stochastic, agentbased, particlebased) simulations become increasingly prevalent in the modeling of complex systems, so does the need to systematically coarsegrain the information they provide. Before even starting to formulate relevant coarsegrained 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 coarsegraining the dynamics of a particlebased model of animal swarming. Our computational datadriven coarsegraining approach extracts two coarse (collective) variables from the detailed particlebased simulations, and helps formulate a lowdimensional 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 datamining 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.
NASA Astrophysics Data System (ADS)
Paasche, H.; Tronicke, J.
20120401
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 multimethod 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 usergiven 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 multidimensional 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
A selflearning particle swarm optimizer for global optimization problems.
Li, Changhe; Yang, Shengxiang; Nguyen, Trung Thanh
20120601
Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called selflearning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two realworld problems show that SLPSO has a superior performance in comparison with several other peer algorithms.
A LyapunovBased Extension to Particle Swarm Dynamics for Continuous Function Optimization
Bhattacharya, Sayantani; Konar, Amit; Das, Swagatam; Han, Sang Yong
20090101
The paper proposes three alternative extensions to the classical globalbest particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the wellknown 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
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
20090801
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 secondorder 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 secondorder design problem. PSO, which is an iterativestochastic 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. Secondorder design of a geodetic levelling network using PSO yields a practically realizable solution. It is also suitable for nonlinear matrix functions that are very often encountered in geodetic network optimization. The fundamentals of the method and a numeric example are given.
Lithography using quantum entangled particles
NASA Technical Reports Server (NTRS)
Williams, Colin (Inventor); Dowling, Jonathan (Inventor)
20030101
A system of etching using quantum entangled particles to get shorter interference fringes. An interferometer is used to obtain an interference fringe. N entangled photons are input to the interferometer. This reduces the distance between interference fringes by n, where again n is the number of entangled photons.
Lithography using quantum entangled particles
NASA Technical Reports Server (NTRS)
Williams, Colin (Inventor); Dowling, Jonathan (Inventor)
20010101
A system of etching using quantum entangled particles to get shorter interference fringes. An interferometer is used to obtain an interference fringe. N entangled photons are input to the interferometer. This reduces the distance between interference fringes by n, where again n is the number of entangled photons.
Crop classification by forward neural network with adaptive chaotic particle swarm optimization.
Zhang, Yudong; Wu, Lenan
20110101
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 graylevel cooccurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a twohiddenlayer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). Kfold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to backpropagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient backpropagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10(7) s.
Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization
NASA Technical Reports Server (NTRS)
SobieszczanskiSobieski, Jaroslaw; Venter, Gerhard
20020101
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 bilevel optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradientbased 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.
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 steadystate and transient motor ripple torque.
NASA Astrophysics Data System (ADS)
Yoon, KyungBeom; Park, WonHee
20150401
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. Preservativetreated 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.
The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.
Han, Gaining; Fu, Weiping; Wang, Wen
20160101
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 timevariant target behavior and obstacle avoidance behavior. Considering the safety and realtime 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 realtime and reliability. PMID:26880881
Automatic Clustering Using Multiobjective Particle Swarm and Simulated Annealing
Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud
20150101
This paper puts forward a new automatic clustering algorithm based on MultiObjective 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 multiobjective based particle swarm optimization (PSO) and the MultiObjective 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
The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.
Han, Gaining; Fu, Weiping; Wang, Wen
20160101
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 timevariant target behavior and obstacle avoidance behavior. Considering the safety and realtime 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 realtime and reliability.
Strength Pareto particle swarm optimization and hybrid EAPSO for multiobjective optimization.
Elhossini, Ahmed; Areibi, Shawki; Dony, Robert
20100101
This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multiobjective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EAPSO algorithms to solve different multiobjective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multiobjective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multiobjective PSO (MOPSO), and the proposed strength Pareto PSO based on different metrics.
Automatic Clustering Using Multiobjective Particle Swarm and Simulated Annealing.
Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud
20150101
This paper puts forward a new automatic clustering algorithm based on MultiObjective 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 multiobjective based particle swarm optimization (PSO) and the MultiObjective 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.
The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm
Han, Gaining; Fu, Weiping; Wang, Wen
20160101
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 timevariant target behavior and obstacle avoidance behavior. Considering the safety and realtime 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 realtime and reliability. PMID:26880881
Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references
NASA Astrophysics Data System (ADS)
Janapati, Ravi Chander; Balaswamy, Ch.; Soundararajan, K.
20160801
Localization is the key research area in Wireless Sensor Networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao Bound (CRB). This censoring scheme can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper Distributed localization algorithm PSO with CRB is proposed. Proposed method shows better results in terms of position accuracy, latency and complexity.
Swarming behavior of gradientresponsive Brownian particles in a porous medium
NASA Astrophysics Data System (ADS)
Grančič, Peter; Štěpánek, František
20120701
Active targeting by Brownian particles in a fluidfilled 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.
Swarming behavior of gradientresponsive Brownian particles in a porous medium.
Grančič, Peter; Štěpánek, František
20120701
Active targeting by Brownian particles in a fluidfilled 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.
NASA Astrophysics Data System (ADS)
Tao, Shanshan; Dong, Sheng; Wang, Zhifeng; Jiang, Wensheng
20160601
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 commonused 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 parameterestimation methods for the maximum entropy distribution. All methods implemented in this study pass the KS 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.
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
20120901
Rayleigh waves have been used increasingly as an appealing tool to obtain nearsurface shear (S)wave velocity profiles. However, inversion of Rayleigh wave dispersion curves is challenging for most localsearch 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 noisefree and three noisecorrupted 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 lowmisfit region less than 15% relative error to further investigate the performance of the proposed inverse procedure. Finally, we inverted a realworld 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.
MultiRobot, MultiTarget Particle Swarm Optimization Search in Noisy Wireless Environments
Kurt Derr; Milos Manic
20090501
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 multirobotmultitarget 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.
Binary particle swarm optimization algorithm assisted to design of plasmonic nanospheres sensor
NASA Astrophysics Data System (ADS)
Kaboli, Milad; Akhlaghi, Majid; Shahmirzaee, Hossein
20160401
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.
Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming
20160101
Mobile sinks can achieve loadbalancing and energyconsumption 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
Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming
20160101
Mobile sinks can achieve loadbalancing and energyconsumption 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.
Thickness of the particle swarm in cosmic ray air showers
NASA Technical Reports Server (NTRS)
Linsley, J.
19850101
The average dispersion in arrival time of air shower particles detected with a scintillator at an impact parameter r is described with accuracy 510% 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 (miniarrays). 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 arraytelescopes.
NASA Astrophysics Data System (ADS)
Suman, A.; Mukerji, T.; Fernandez Martinez, J.
20101201
Time lapse seismic data has begun to play an important role in reservoir characterization, management and monitoring. It can provide information on the dynamics of fluids in the reservoir based on the relation between variations of seismic signals and movement of hydrocarbons and changes in formation pressure. Reservoir monitoring by repeated seismic or time lapse surveys can help in reducing the uncertainties attached to reservoir models. In combination with geological and flow modeling as a part of history matching process it can provide better description of the reservoir and thus better reservoir forecasting. However joint inversion of seismic and flow data for reservoir parameter is highly nonlinear and complex. Stochastic optimization based inversion has shown very good results in integration of timelapse seismic and production data in reservoir history matching. In this paper we have used a family of particle swarm optimizers for inversion of semisynthetic Norne field data set. We analyze the performance of the different PSO optimizers, both in terms of exploration and convergence rate. Finally we also show some promising and preliminary results of the application of differential evolution. All of the versions of PSO provide an acceptable match with the original synthetic model. The advantage of using global optimization method is that uncertainty can be assessed near the optimum point. To assess uncertainty near the optimum point we keep track of all particles over all iterations that have an objective function value below a selected cutoff. With these particles we plot the best, Etype and IQR (Inter quartile range) of porosity and permeability for each version of PSO. To compute uncertainty measures using a stochastic optimizer algorithm care has to be taken not to oversample the optimal point. We keep track of the evolution of the median distance between the global best in each of the iterations and the particles of the swarm. When this distance is
Dynamic topology multi force particle swarm optimization algorithm and its application
NASA Astrophysics Data System (ADS)
Chen, Dongning; Zhang, Ruixing; Yao, Chengyu; Zhao, Zheyu
20160101
Particle swarm optimization (PSO) algorithm is an effective bioinspired 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 fitnessdriven edgechanging (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 wellknown 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.
Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming
20160101
Mobile sinks can achieve loadbalancing and energyconsumption 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
NASA Astrophysics Data System (ADS)
Zhao, Yong; Chen, Genliang; Wang, Hao; Lin, Zhongqin
20130701
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 subswarm is introduced to represent the different mechanisms generated by the type synthesis, and a competitive mechanism is employed between the subswarms 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.
A particle swarm model for estimating reliability and scheduling system maintenance
NASA Astrophysics Data System (ADS)
Puzis, Rami; Shirtz, Dov; Elovici, Yuval
20160501
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 componentlevel maintenance policies. Results show that appropriately scheduled componentlevel maintenance greatly reduces the cost of upholding an acceptable level of reliability by reducing the need in systemwide maintenance.
Yau, HerTerng; Hung, TzuHsiang; Hsieh, ChiaChun
20120101
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
Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun
20160101
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
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.
20160601
Particle Swarm Optimization (PSO) is used to prune the search space of a lowthrust trajectory transfer from a highaltitude, Earth orbit to a Lagrange point orbit in the EarthMoon 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 runtime 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.
Accurate FDTD modelling for dispersive media using rational function and particle swarm optimisation
NASA Astrophysics Data System (ADS)
Chung, Haejun; Ha, SangGyu; Choi, Jaehoon; Jung, KyungYoung
20150701
This article presents an accurate finitedifference 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 matrixsolving 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.
Sung, WenTsai; Chiang, YenChun
20121201
This study examines wireless sensor network with realtime 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 multisensors 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 multiphysiological 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.
Using Animal Instincts to Design Efficient Biomedical Studies via Particle Swarm Optimization
Qiu, Jiaheng; Chen, RayBing; Wang, Weichung; Wong, Weng Kee
20140101
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 gardenvariety 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
Sung, WenTsai; Chiang, YenChun
20121201
This study examines wireless sensor network with realtime 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 multisensors 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 multiphysiological 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
MultiObjective Bidding Strategy for Genco Using NonDominated Sorting Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Saksinchai, Apinat; Boonchuay, Chanwit; Ongsakul, Weerakorn
20100601
This paper proposes a multiobjective bidding strategy for a generation company (GenCo) in uniform price spot market using nondominated sorting particle swarm optimization (NSPSO). Instead of using a tradeoff technique, NSPSO is introduced to solve the multiobjective 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 nondominated 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.
Yau, HerTerng; Hung, TzuHsiang; Hsieh, ChiaChun
20120101
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.
NASA Astrophysics Data System (ADS)
OrdóñezHurtado, R. H.; DuarteMermoud, M. A.
20120101
It is undoubtedly important to be able to ensure the existence of a common quadratic Lyapunov function (CQLF) for a given switched system because this is proof of its asymptotic stability, but equally important is the ability to calculate it in order to obtain more specific information about the behaviour of the switched system under analysis. This article describes the development of a new methodology for calculating a CQLF based on particle swarm optimisation (PSO) once the existence of a CQLF has been assured. Several comparative analyses are presented to show the strengths and advantages of the proposed methodology.
Luo, Xiongbiao Email: Ying.Wan@student.uts.edu.au; Wan, Ying Email: Ying.Wan@student.uts.edu.au; He, Xiangjian
20150415
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 stateoftheart 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
Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun
20160101
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
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
20091001
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.
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.
20150701
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.
An IntegerCoded Chaotic Particle Swarm Optimization for Traveling Salesman Problem
NASA Astrophysics Data System (ADS)
Yue, Chen; YanDuo, Zhang; Jing, Lu; Hui, Tian
Traveling Salesman Problem (TSP) is one of NPhard 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, Integercoded 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 20citys 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.
Sun, Jun; Palade, Vasile; Wu, Xiaojun; Fang, Wei
20140101
Hidden Markov Models (HMMs) are powerful tools for multiple sequence alignment (MSA), which is known to be an NPcomplete and important problem in bioinformatics. Learning HMMs is a difficult task, and many metaheuristic methods, including particle swarm optimization (PSO), have been used for that. In this paper, a new variant of PSO, called the random drift particle swarm optimization (RDPSO) algorithm, is proposed to be used for HMM learning tasks in MSA problems. The proposed RDPSO algorithm, inspired by the free electron model in metal conductors in an external electric field, employs a novel set of evolution equations that can enhance the global search ability of the algorithm. Moreover, in order to further enhance the algorithmic performance of the RDPSO, we incorporate a diversity control method into the algorithm and, thus, propose an RDPSO with diversityguided search (RDPSODGS). The performances of the RDPSO, RDPSODGS and other algorithms are tested and compared by learning HMMs for MSA on two wellknown benchmark data sets. The experimental results show that the HMMs learned by the RDPSO and RDPSODGS are able to generate better alignments for the benchmark data sets than other most commonly used HMM learning methods, such as the BaumWelch and other PSO algorithms. The performance comparison with wellknown MSA programs, such as ClustalW and MAFFT, also shows that the proposed methods have advantages in multiple sequence alignment.
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
20150101
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 (CSPSO) 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 graylevel probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CSPSO 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
Robinson, Y Harold; Rajaram, M
20150101
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 energyaware 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 loopfree 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 loopfree 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 loopfree paths by using PSO technique. PMID:26819966
New hybrid genetic particle swarm optimization algorithm to design multizone binary filter.
Lin, Jie; Zhao, Hongyang; Ma, Yuan; Tan, Jiubin; Jin, Peng
20160516
The binary phase filters have been used to achieve an optical needle with small lateral size. Designing a binary phase filter is still a scientific challenge in such fields. In this paper, a hybrid genetic particle swarm optimization (HGPSO) algorithm is proposed to design the binary phase filter. The HGPSO algorithm includes selfadaptive parameters, recombination and mutation operations that originated from the genetic algorithm. Based on the benchmark test, the HGPSO algorithm has achieved global optimization and fast convergence. In an easytoperform optimizing procedure, the iteration number of HGPSO is decreased to about a quarter of the original particle swarm optimization process. A multizone binary phase filter is designed by using the HGPSO. The long depth of focus and high resolution are achieved simultaneously, where the depth of focus and focal spot transverse size are 6.05λ and 0.41λ, respectively. Therefore, the proposed HGPSO can be applied to the optimization of filter with multiple parameters. PMID:27409895
EnergyAware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks
Robinson, Y. Harold; Rajaram, M.
20150101
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 energyaware 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 loopfree 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 loopfree 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 loopfree paths by using PSO technique. PMID:26819966
Parameter identification of robot manipulators: a heuristic particle swarm search approach.
Yan, Danping; Lu, Yongzhong; Levy, David
20150101
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
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
20150101
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 (CSPSO) 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 graylevel probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CSPSO 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.
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
20150101
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 (CSPSO) 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 graylevel probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CSPSO 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
Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine
Yuan, Hua; Huang, Jianping; Cao, Chenzhong
20090101
Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structureactivity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers. PMID:19742136
Parameter identification of robot manipulators: a heuristic particle swarm search approach.
Yan, Danping; Lu, Yongzhong; Levy, David
20150101
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.
Robinson, Y Harold; Rajaram, M
20150101
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 energyaware 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 loopfree 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 loopfree 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 loopfree paths by using PSO technique.
Biochemical systems identification by a random drift particle swarm optimization approach
20140101
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 datadriven 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 gradientbased 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 noisefree 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
NASA Astrophysics Data System (ADS)
SoltaniMohammadi, Saeed; Safa, Mohammad; Mokhtari, Hadi
20161001
One of the most important stages in complementary exploration is optimal designing the additional drilling pattern or defining the optimum number and location of additional boreholes. Quite a lot research has been carried out in this regard in which for most of the proposed algorithms, kriging variance minimization as a criterion for uncertainty assessment is defined as objective function and the problem could be solved through optimization methods. Although kriging variance implementation is known to have many advantages in objective function definition, it is not sensitive to local variability. As a result, the only factors evaluated for locating the additional boreholes are initial data configuration and variogram model parameters and the effects of local variability are omitted. In this paper, with the goal of considering the local variability in boundaries uncertainty assessment, the application of combined variance is investigated to define the objective function. Thus in order to verify the applicability of the proposed objective function, it is used to locate the additional boreholes in Esfordi phosphate mine through the implementation of metaheuristic optimization methods such as simulated annealing and particle swarm optimization. Comparison of results from the proposed objective function and conventional methods indicates that the new changes imposed on the objective function has caused the algorithm output to be sensitive to the variations of grade, domain's boundaries and the thickness of mineralization domain. The comparison between the results of different optimization algorithms proved that for the presented case the application of particle swarm optimization is more appropriate than simulated annealing.
NASA Astrophysics Data System (ADS)
Lin, ChengJian; Lee, ChiYung
20100401
This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for nonlinear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods.
hydroPSO: A Versatile Particle Swarm Optimisation R Package for Calibration of Environmental Models
NASA Astrophysics Data System (ADS)
ZambranoBigiarini, M.; Rojas, R.
20120401
Particle Swarm Optimisation (PSO) is a recent and powerful populationbased 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 multidimensional searchspace according to its own experience (bestknown personal position) and the one of its neighbours in the swarm (bestknown 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 suboptimal 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 platformindependent 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 modelindependent, 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 finetune hydroPSO are: four alternative topologies, several types of inertia weight, timevariant acceleration coefficients, timevariant 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
NASA Astrophysics Data System (ADS)
Izah Anuar, Nurul; Saptari, Adi
20160201
This paper addresses the types of particle representation (encoding) procedures in a populationbased stochastic optimization technique in solving scheduling problems known in the jobshop 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 Jobshop 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 randomkey encoding scheme are used in this study. These procedures have been tested on FT06 and FT10 benchmark problems available in the ORLibrary, 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.
Xu, ShengHua; Liu, JiPing; Zhang, FuHao; Wang, Liang; Sun, LiJian
20150101
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 wellknown solution for this benchmark problem is also outlined in the following. PMID:26343655
NASA Astrophysics Data System (ADS)
Xu, Jiuping; Zeng, Ziqiang; Han, Bernard; Lei, Xiao
20130701
This article presents a dynamic programmingbased particle swarm optimization (DPbased PSO) algorithm for solving an inventory management problem for largescale construction projects under a fuzzy random environment. By taking into account the purchasing behaviour and strategy under rules of international bidding, a multiobjective 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 optimisticpessimistic index. The iterative nature of the authors' model motivates them to develop a DPbased 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.
On the application of Particle Swarm Optimization strategies on Scholtewave inversion
NASA Astrophysics Data System (ADS)
Wilken, D.; Rabbel, W.
20120701
We investigate different aspects concerning the application of swarm intelligence optimization to the inversion of Scholtewave phaseslowness frequency (pf) 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 pf 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 Scholtewave 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 Scholtewave 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 2D 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
Clothed Particles in Quantum Electrodynamics and Quantum Chromodynamics
NASA Astrophysics Data System (ADS)
Shebeko, Alexander
20160301
The notion of clothing in quantum field theory (QFT), put forward by Greenberg and Schweber and developed by M. Shirokov, is applied in quantum electrodynamics (QED) and quantum chromodynamics (QCD). Along the guideline we have derived a novel analytic expression for the QED Hamiltonian in the clothed particle representation (CPR). In addition, we are trying to realize this notion in QCD (to be definite for the gauge group SU(3)) when drawing parallels between QCD and QED.
Parameter Identification of Robot Manipulators: A Heuristic Particle Swarm Search Approach
Yan, Danping; Lu, Yongzhong; Levy, David
20150101
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
Quantum Gravity: The View From Particle Physics
NASA Astrophysics Data System (ADS)
Nicolai, Hermann
This lecture reviews aspects of and prospects for progress towards a theory of quantum gravity from a particle physics perspective, also paying attention to recent findings of the LHC experiments at CERN.
Particle swarm optimization for optimal sensor placement in ultrasonic SHM systems
NASA Astrophysics Data System (ADS)
Blanloeuil, Philippe; Nurhazli, Nur A. E.; Veidt, Martin
20160401
A Particle Swarm Optimization (PSO) algorithm is used to improve sensors placement in an ultrasonic Structural Health Monitoring (SHM) system where the detection is performed through the beamforming imaging algorithm. The imaging algorithm reconstructs the defect image and estimates its location based on analytically generated signals, considering circular through hole damage in an aluminum plate as the tested structure. Then, the PSO algorithm changes the position of sensors to improve the accuracy of the detection. Thus, the two algorithms are working together iteratively to optimize the system configuration, taking into account a complete modeling of the SHM system. It is shown that this approach can provide good sensors placements for detection of multiple defects in the target area, and for different numbers of sensors.
Wang, Jiaxi; Lin, Boliang; Jin, Junchen
20160101
The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for highspeed train maintenance activities. This paper presents a 01 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
Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu
20160815
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 decisionmaking 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 multiobjective 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. Multiobjective Particle Swarm Algorithm is proposed to solve the model, and the decisionmakers 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 decisionmakers/stakeholders to make decision.
Particle Swarm Optimization Method Based on Chaotic Local Search and Roulette Wheel Mechanism
NASA Astrophysics Data System (ADS)
Xia, Xiaohua
Combining the particle swarm optimization (PSO) technique with the chaotic local search (CLS) and roulette wheel mechanism (RWM), an efficient optimization method solving the constrained nonlinear optimization problems is presented in this paper. PSO can be viewed as the global optimizer while the CLS and RWM are employed for the local search. Thus, the possibility of exploring a global minimum in problems with many local optima is increased. The search will continue until a termination criterion is satisfied. Benefit from the fast globally converging characteristics of PSO and the effective local search ability of CLS and RWM, the proposed method can obtain the global optimal results quickly which was tested for six benchmark optimization problems. And the improved performance comparing with the standard PSO and genetic algorithm (GA) testified its validity.
Extended particle swarm optimisation method for folding protein on triangular lattice.
Guo, Yuzhen; Wu, Zikai; Wang, Ying; Wang, Yong
20160201
In this study, the authors studied the protein structure prediction problem by the twodimensional hydrophobicpolar model on triangular lattice. Particularly the noncompact 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.
NASA Astrophysics Data System (ADS)
Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng
20160201
Spotwelding 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 algorithmparticle swarm optimization (GAPSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collisionfree 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.
Multiterminal pipe routing by Steiner minimal tree and particle swarm optimisation
NASA Astrophysics Data System (ADS)
Liu, Qiang; Wang, Chengen
20120801
Computeraided design of pipe routing is of fundamental importance for complex equipments' developments. In this article, nonrectilinear 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 aeroengineintegrated 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 nonrectilinear 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.
Extended particle swarm optimisation method for folding protein on triangular lattice.
Guo, Yuzhen; Wu, Zikai; Wang, Ying; Wang, Yong
20160201
In this study, the authors studied the protein structure prediction problem by the twodimensional hydrophobicpolar model on triangular lattice. Particularly the noncompact 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
DOA estimation for local scattered CDMA signals by particle swarm optimization.
Chang, JhihChung
20120101
This paper deals with the directionofarrival (DOA) estimation of local scattered codedivision 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 highresolution 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.
Li, Junqing; Pan, Quanke; Mao, Kun
20140101
A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILSbased local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron's benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm. PMID:24883414
NASA Astrophysics Data System (ADS)
Petković, Dalibor; Gocic, Milan; Shamshirband, Shahaboddin; Qasem, Sultan Noman; Trajkovic, Slavisa
20160801
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 (RBFNPSO) and radial basis function network with back propagation (RBFNBP) were used in this investigation. The FAO56 PenmanMonteith equation was used as reference equation to estimate ET0 for Serbia during the period of 19802010. The obtained simulation results confirmed the proposed models and were analyzed using the root meansquare error (RMSE), the mean absolute error (MAE), and the coefficient of determination ( R 2). The analysis showed that the RBFNPSO had better statistical characteristics than RBFNBP and can be helpful for the ET0 estimation.
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.
20151101
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.
NASA Astrophysics Data System (ADS)
Lin, Juan; Liu, Chenglian; Guo, Yongning
20141001
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 sourcemodeling 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.
Jin, Junchen
20160101
The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for highspeed train maintenance activities. This paper presents a 01 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
Optimal Control for a Parallel Hybrid Hydraulic Excavator Using Particle Swarm Optimization
Wang, Dongyun; Guan, Chen
20130101
Optimal control using particle swarm optimization (PSO) is put forward in a parallel hybrid hydraulic excavator (PHHE). A powertrain mathematical model of PHHE is illustrated along with the analysis of components' parameters. Then, the optimal control problem is addressed, and PSO algorithm is introduced to deal with this nonlinear optimal problem which contains lots of inequality/equality constraints. Then, the comparisons between the optimal control and rulebased one are made, and the results show that hybrids with the optimal control would increase fuel economy. Although PSO algorithm is offline optimization, still it would bring performance benchmark for PHHE and also help have a deep insight into hybrid excavators. PMID:23818832
NASA Astrophysics Data System (ADS)
Zhang, ChuanXin; Yuan, Yuan; Zhang, HaoWei; Shuai, Yong; Tan, HePing
20160901
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.
Energy and operation management of a microgrid using particle swarm optimization
NASA Astrophysics Data System (ADS)
Radosavljević, Jordan; Jevtić, Miroljub; Klimenta, Dardan
20160501
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 gridconnected microgrid including fuel cell, gasfired 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.
Infrared face recognition based on binary particle swarm optimization and SVMwrapper model
NASA Astrophysics Data System (ADS)
Xie, Zhihua; Liu, Guodong
20151001
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.
ARIMA Model Estimated by Particle Swarm Optimization Algorithm for Consumer Price Index Forecasting
NASA Astrophysics Data System (ADS)
Wang, Hongjie; Zhao, Weigang
This paper presents an ARIMA model which uses particle swarm optimization algorithm (PSO) for model estimation. Because the traditional estimation method is complex and may obtain very bad results, PSO which can be implemented with ease and has a powerful optimizing performance is employed to optimize the coefficients of ARIMA. In recent years, inflation and deflation plague the world moreover the consumer price index (CPI) which is a measure of the average price of consumer goods and services purchased by households is usually observed as an important indicator of the level of inflation, so the forecast of CPI has been focused on by both scientific community and relevant authorities. Furthermore, taking the forecast of CPI as a case, we illustrate the improvement of accuracy and efficiency of the new method and the result shows it is predominant in forecasting.
Online energy management for HEV based on particle swarm optimization
NASA Astrophysics Data System (ADS)
Caux, S.; WanderleyHonda, D.; Hissel, D.; Fadel, M.
20110501
This study considers a Hybrid Electrical Vehicle supplied by a Fuel Cell stack and supercapacitors used as Storage Element. In such an application, real time energy management is of paramount importance in order to increase autonomy and be able to deal online with perturbed power demand. Many offline power flow optimization principles are available but online algorithms are preferred and should be derived for optimal management of the instantaneous power splitting between the different available power sources. Based on particle swarm optimization algorithm, this study defines the parameters tuning of such algorithm. The final power splitting allows not only recovering energy braking but also is robust to some disturbances occurring during the trip. The solution provides goodquality and highrobustness results in a certain class of mission profile and power disturbance.
A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy
Pedrycz, Witold; Liu, Jin
20140101
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 problemoriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSOIVL) 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 PSOIVL. The effectiveness of the PSOIVL stresses a usefulness of augmenting evolutionary algorithms by problemoriented domain knowledge. PMID:24587746
PID controller design of nonlinear systems using an improved particle swarm optimization approach
NASA Astrophysics Data System (ADS)
Chang, WeiDer; Shih, ShunPeng
20101101
In this paper, an improved particle swarm optimization is presented to search for the optimal PID controller gains for a class of nonlinear systems. The proposed algorithm is to modify the velocity formula of the general PSO systems in order for improving the searching efficiency. In the improved PSObased nonlinear PID control system design, three PID control gains, i.e., the proportional gain Kp, integral gain Ki, and derivative gain Kd are required to form a parameter vector which is called a particle. It is the basic component of PSO systems and many such particles further constitute a population. To derive the optimal PID gains for nonlinear systems, two principle equations, the modified velocity updating and position updating equations, are employed to move the positions of all particles in the population. In the meanwhile, an objective function defined for PID controller optimization problems may be minimized. To validate the control performance of the proposed method, a typical nonlinear system control, the inverted pendulum tracking control, is illustrated. The results testify that the improved PSO algorithm can perform well in the nonlinear PID control system design.
Particle Swarm Optimization for inverse modeling of solute transport in fractured gneiss aquifer.
Abdelaziz, Ramadan; ZambranoBigiarini, Mauricio
20140801
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 (MODFLOW2005MT3DMS) 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 (SPSO2011), 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 doubleporosity 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.
Particle Swarm Optimization for inverse modeling of solute transport in fractured gneiss aquifer
NASA Astrophysics Data System (ADS)
Abdelaziz, Ramadan; ZambranoBigiarini, Mauricio
20140801
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 (MODFLOW2005MT3DMS) 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 (SPSO2011), 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 doubleporosity 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.
Mutation particle swarm optimization of the BPPID controller for piezoelectric ceramics
NASA Astrophysics Data System (ADS)
Zheng, Huaqing; Jiang, Minlan
20160101
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 BPPID. That designed a better selfadaptive 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 MPSOBPPID. 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 MPSOBPPID can complete controlling the controlled plant with higher speed and accuracy. Therefore, the MPSOBPPID is applied to the piezoelectric ceramic. It can effectively overcome the hysteresis, nonlinearity of the piezoelectric ceramic. In the experiment, compared with BPPID and PSOBPPID, it proved that MPSO is effective and the MPSOBPPID has stronger adaptability and robustness.
Discrete particle swarm optimization for identifying community structures in signed social networks.
Cai, Qing; Gong, Maoguo; Shen, Bo; Ma, Lijia; Jiao, Licheng
20141001
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 stateoftheart approaches on both synthetic and realworld signed networks demonstrate that the proposed method is effective and promising. PMID:24856248
NASA Astrophysics Data System (ADS)
Wu, Q.; Xiong, F.; Wang, F.; Xiong, Y.
20161001
In order to reduce the computational time, a fully parallel implementation of the particle swarm optimization (PSO) algorithm on a graphics processing unit (GPU) is presented. Instead of being executed on the central processing unit (CPU) sequentially, PSO is executed in parallel via the GPU on the compute unified device architecture (CUDA) platform. The processes of fitness evaluation, updating of velocity and position of all particles are all parallelized and introduced in detail. Comparative studies on the optimization of four benchmark functions and a trajectory optimization problem are conducted by running PSO on the GPU (GPUPSO) and CPU (CPUPSO). The impact of design dimension, number of particles and size of the threadblock in the GPU and their interactions on the computational time is investigated. The results show that the computational time of the developed GPUPSO is much shorter than that of CPUPSO, with comparable accuracy, which demonstrates the remarkable speedup capability of GPUPSO.
Arasomwan, Martins Akugbe; Adewumi, Aderemi Oluyinka
20130101
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 (LDIWPSO) 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 LDIWPSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIWPSO. The major goal of this paper is to experimentally establish the fact that LDIWPSO 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 LDIWPSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five wellknown benchmark optimization problems were used to show the outstanding performance of LDIWPSO 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 LDIWPSO with the latter outperforming both in the simulation experiments conducted. PMID:24324383
NASA Astrophysics Data System (ADS)
Quaranta, Giuseppe; Monti, Giorgio; Marano, Giuseppe Carlo
20101001
Many of the proposed approaches for nonlinear systems control are developed under the assumption that all involved parameters are known in advance. Unfortunately, their estimation is not so simple because the nature of the nonlinear behaviors is very complex in the most part of the cases. In view of this complication, parameters identification of nonlinear oscillators has attracted increasing interests in various research fields: from a pure mathematical pointofview, parameters identification can be formalized as a multidimensional optimization problem, typically over real bounded domains. In doing this, the use of the socalled nonclassical methods based on soft computing theories seems to be promising because they do not require a priori information and the robustness of the identification against the noise contamination is satisfactory. However, further studies are required to evaluate the general effectiveness of these methodologies. In this sense, the paper addresses the consistency of two classes of soft computing based methods for the identification of Van der PolDuffing oscillators. A large numerical investigation has been conducted to evaluate the performances of six differential evolution algorithms (including a modified differential evolution algorithm proposed by the authors) and four swarm intelligence based algorithms (including a chaotic particle swarm optimization algorithm). Single well, double well and doublehump oscillators are identified and noisy system responses are considered in order to evaluate the robustness of the identification processes. The investigated soft computing techniques behave very well and thus they are suitable for practical applications.
NASA Astrophysics Data System (ADS)
Zhang, Yanjun; Zhao, Yu; Fu, Xinghu; Xu, Jinrui
20161001
A novel particle swarm optimization algorithm based on adaptive inertia weight and chaos optimization is proposed for extracting the features of Brillouin scattering spectra. Firstly, the adaptive inertia weight parameter of the velocity is introduced to the basic particle swarm algorithm. Based on the current iteration number of particles and the adaptation value, the algorithm can change the weight coefficient and adjust the iteration speed of searching space for particles, so the local optimization ability can be enhanced. Secondly, the logical selfmapping chaotic search is carried out by using the chaos optimization in particle swarm optimization algorithm, which makes the particle swarm optimization algorithm jump out of local optimum. The novel algorithm is compared with finite element analysisLevenberg Marquardt algorithm, particle swarm optimizationLevenberg Marquardt algorithm and particle swarm optimization algorithm by changing the linewidth, the signaltonoise ratio and the linear weight ratio of Brillouin scattering spectra. Then the algorithm is applied to the feature extraction of Brillouin scattering spectra in different temperatures. The simulation analysis and experimental results show that this algorithm has a high fitting degree and small Brillouin frequency shift error for different linewidth, SNR and linear weight ratio. Therefore, this algorithm can be applied to the distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can effectively improve the accuracy of Brillouin frequency shift extraction.
The contribution of particle swarm optimization to threedimensional slope stability analysis.
Kalatehjari, Roohollah; Rashid, Ahmad Safuan A; Ali, Nazri; Hajihassani, Mohsen
20140101
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 twodimensional (2D) slope stability analysis. This paper applied PSO in threedimensional (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 PLAXI3D 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
Huang, X N; Ren, H P
20160101
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 prestimulus steady state timely. In this paper, the GRN is modeled using the MichaelisMenten 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 multivariable, multiobjective, and multipeak optimization problem, which is difficult to acquire satisfactory solutions especially highquality solutions. A new bestneighbor 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 highquality 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
Bayesian network structure learning based on the chaotic particle swarm optimization algorithm.
Zhang, Q; Li, Z; Zhou, C J; Wei, X P
20130101
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
Kamberaj, Hiqmet
20150928
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 LennardJones 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.
The Contribution of Particle Swarm Optimization to ThreeDimensional Slope Stability Analysis
A Rashid, Ahmad Safuan; Ali, Nazri
20140101
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 twodimensional (2D) slope stability analysis. This paper applied PSO in threedimensional (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 PLAXI3D 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
NASA Astrophysics Data System (ADS)
Kamberaj, Hiqmet
20150901
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 LennardJones 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.
NASA Astrophysics Data System (ADS)
Liu, TunDong; Fan, TianE.; Shao, GuiFang; Zheng, JiWen; Wen, YuHua
20140801
Bimetallic nanoparticles, enclosed by highindex facets, have great catalytic activity and selectivity owing to the synergy effects of highindex 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 Ptbased bimetallic nanoparticles with highindex 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. PtAg nanoparticles tend to form PtAg coreshell structure. PtCu nanoparticles are preferred to take multishell structure with Cu on the outer surface while PtPd nanoparticles present a mixing structure in the interior and Pddominated surface. Atomic distribution and bonding characteristics were applied to further characterize the structural features of Ptbased nanoparticles. This study provides an important insight into the structural stability and features of Ptbased nanoparticles with different alloys.
NanoSWARM  A nanosatellite mission to measure particles and fields around the Moon
NASA Astrophysics Data System (ADS)
GarrickBethell, Ian; Russell, Christopher; Pieters, Carle; Weiss, Benjamin; Halekas, Jasper; Poppe, Andrew; Larson, Davin; Lawrence, David; Elphic, Richard; Hayne, Paul; Blakely, Richard; Kim, KhanHyuk; Choi, YoungJun; Jin, Ho; Hemingway, Doug; Nayak, Michael; PuigSuari, Jordi; Jaroux, Belgacem; Warwick, Steven
20150401
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 smallscale magnetospheres. To accomplish these goals, NanoSWARM targets scientifically rich features on the Moon known as swirls. Swirls are highalbedo features correlated with strong magnetic fields and low surfacewater. NanoSWARM cubesats will make the first nearsurface (<500 m altitude) measurements of solar wind flux and magnetic fields at swirls. NanoSWARM cubesats will also perform lowaltitude 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 highheritage 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.
NanoSWARM: A Nanosatellite Mission to Measure Particles and Fields Around the Moon
NASA Astrophysics Data System (ADS)
GarrickBethell, I.
20151201
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 smallscale magnetospheres. To accomplish these goals, NanoSWARM targets scientifically rich features on the Moon known as swirls. Swirls are highalbedo features correlated with strong magnetic fields and low surfacewater. NanoSWARM cubesats will make the first nearsurface (<1 km altitude) measurements of solar wind flux and magnetic fields at swirls. NanoSWARM cubesats will also perform lowaltitude 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 highheritage 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.
NASA Astrophysics Data System (ADS)
Siade, A. J.; Prommer, H.; Welter, D.
20141201
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 modelstructure 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 leastsquares objective function is not straightforward. This is further compounded by the presence of potential sources of modelstructure 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 tradeoffs 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 multidimensional Pareto front that illustrates the tradeoffs 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
NASA Astrophysics Data System (ADS)
He, Yaoyao; Yang, Shanlin; Xu, Qifa
20130701
In order to solve the model of shortterm 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.
NASA Astrophysics Data System (ADS)
Shirazi, Masoud Jahromi; Vatankhah, Ramin; Boroushaki, Mehrdad; Salarieh, Hassan; Alasty, Aria
20120201
In this paper, particle swarm optimization (PSO) is applied to synchronize chaotic systems in presence of parameter uncertainties and measurement noise. Particle swarm optimization is an evolutionary algorithm which is introduced by Kennedy and Eberhart. This algorithm is inspired by birds flocking. Optimization algorithms can be applied to control by defining an appropriate cost function that guarantees stability of system. In presence of environment noise and parameter uncertainty, robustness plays a crucial role in succeed of controller. Since PSO needs only rudimentary information about the system, it can be a suitable algorithm for this case. Simulation results confirm that the proposed controller can handle the uncertainty and environment noise without any extra information about them. A comparison with some earlier works is performed during simulations.
NASA Astrophysics Data System (ADS)
Lazzús, J. A.; LópezCaraballo, C. H.; Rojas, P.; Salfate, I.; Rivera, M.; PalmaChilla, L.
20160501
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.
Quantum and classical dissipation of charged particles
IbarraSierra, V.G.; AnzaldoMeneses, A.; Cardoso, J.L.; HernándezSaldaña, H.; Kunold, A.; RoaNeri, J.A.E.
20130815
A Hamiltonian approach is presented to study the two dimensional motion of damped electric charges in time dependent electromagnetic fields. The classical and the corresponding quantum mechanical problems are solved for particular cases using canonical transformations applied to Hamiltonians for a particle with variable mass. Green’s function is constructed and, from it, the motion of a Gaussian wave packet is studied in detail.  Highlights: •Hamiltonian of a damped charged particle in time dependent electromagnetic fields. •Exact Green’s function of a charged particle in time dependent electromagnetic fields. •Time evolution of a Gaussian wave packet of a damped charged particle. •Classical and quantum dynamics of a damped electric charge.
Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm
Naebi, Mohammad; Saberi, Eshaghali; Risbaf Fakour, Sirous; Naebi, Ahmad; Hosseini Tabatabaei, Somayeh; Ansari Moghadam, Somayeh; Bozorgmehr, Elham; Davtalab Behnam, Nasim; Azimi, Hamidreza
20160101
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
Trajectory planning of freefloating space robot using Particle Swarm Optimization (PSO)
NASA Astrophysics Data System (ADS)
Wang, Mingming; Luo, Jianjun; Walter, Ulrich
20150701
This paper investigates the application of Particle Swarm Optimization (PSO) strategy to trajectory planning of the kinematically redundant space robot in freefloating 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 freefloating 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 degreeoffreedom (DOF) redundant manipulator mounted on a freefloating spacecraft and demonstrate the effectiveness of the proposed method.
A divideandconquer strategy with particle swarm optimization for the job shop scheduling problem
NASA Astrophysics Data System (ADS)
Zhang, Rui; Wu, Cheng
20100701
An optimization algorithm based on the 'divideandconquer' methodology is proposed for solving large job shop scheduling problems with the objective of minimizing total weighted tardiness. The algorithm adopts a noniterative framework. It first searches for a promising decomposition policy for the operation set by using a simulated annealing procedure in which the solutions are evaluated with reference to the upper bound and the lower bound of the final objective value. Subproblems are then constructed according to the output decomposition policy and each subproblem is related to a subset of operations from the original operation set. Subsequently, all these subproblems are sequentially solved by a particle swarm optimization algorithm, which leads directly to a feasible solution to the original largescale scheduling problem. Numerical computational experiments are carried out for both randomly generated test problems and the realworld production data from a large speedreducer factory in China. Results show that the proposed algorithm can achieve satisfactory solution quality within reasonable computational time for largescale job shop scheduling problems.
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 costeffective technique. But their training, usually with backpropagation 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.
20150101
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 ANNEvolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634
Automatic Calibration of a SemiDistributed Hydrologic Model Using Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Bekele, E. G.; Nicklow, J. W.
20051201
Hydrologic simulation models need to be calibrated and validated before using them for operational predictions. Spatiallydistributed 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 newlydeveloped calibration model is integrated to the U.S. Department of Agriculture's Soil and Water Assessment Tool (SWAT). SWAT is a physicallybased, semidistributed 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.
Hsu, ChenChien; Lin, GengYu
20090701
In this paper, a particle swarm optimization (PSO) based approach is proposed to derive an optimal digital controller for redesigned digital systems having an interval plant based on timeresponse resemblance of the closedloop systems. Because of difficulties in obtaining timeresponse envelopes for interval systems, the design problem is formulated as an optimization problem of a cost function in terms of aggregated deviation between the step responses corresponding to extremal energies of the redesigned digital system and those of their continuous counterpart. A proposed evolutionary framework incorporating three PSOs is subsequently presented to minimize the cost function to derive an optimal set of parameters for the digital controller, so that step response sequences corresponding to the extremal sequence energy of the redesigned digital system suitably approximate those of their continuous counterpart under the perturbation of the uncertain plant parameters. Computer simulations have shown that redesigned digital systems incorporating the PSOderived digital controllers have better system performance than those using conventional openloop discretization methods.
NASA Astrophysics Data System (ADS)
Aranza, M. F.; Kustija, J.; Trisno, B.; Hakim, D. L.
20160401
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 ZieglerNichols (ZN), gainphase margin, Root Locus, Minimum Variance dan Gain Scheduling however these methods are not optimal to control systems that nonlinear and have highorde, 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 ZieglerNichols.
NASA Astrophysics Data System (ADS)
Sameen, Maher Ibrahim; Pradhan, Biswajeet
20160601
In this study, we propose a novel builtup spectral index which was developed by using particleswarmoptimization (PSO) technique for Worldview2 images. PSO was used to select the relevant bands from the eight (8) spectral bands of Worldview2 image and then were used for index development. Multiobiective optimization was used to minimize the number of selected spectral bands and to maximize the classification accuracy. The results showed that the most important and relevant spectral bands among the eight (8) bands for builtup area extraction are band4 (yellow) and band7 (NIR1). Using those relevant spectral bands, the final spectral index was form ulated by developing a normalized band ratio. The validation of the classification result using the proposed spectral index showed that our novel spectral index performs well compared to the existing WV BI index. The accuracy assessment showed that the new proposed spectral index could extract builtup areas from Worldview2 image with an area under curve (AUC) of (0.76) indicating the effectiveness of the developed spectral index. Further improvement could be done by using several datasets during the index development process to ensure the transferability of the index to other datasets and study areas.
NASA Astrophysics Data System (ADS)
Verma, Harish Kumar; Pal, Sandeep
20160601
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 lowcontrast 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.
Modeling dynamic urban growth using hybrid cellular automata and particle swarm optimization
NASA Astrophysics Data System (ADS)
Rabbani, Amirhosein; Aghababaee, Hossein; Rajabi, Mohammad A.
20120101
Conventional rasterbased cellular automata (CA) confront many difficulties because of cell size and neighborhood sensitivity. Alternatively, vector CAbased models are very complex and difficult to implement. We present a hybrid cellular automata (HCA) model as a combination of cellular structure and vector concept. The space is still defined by a set of cells, but rasterized spatial objects are also utilized in the structure of transition rules. Particle swarm optimization (PSO) is also used to calculate the urbanization probability of cells based on their distance from the development parameters. The proposed model is applied to Landsat satellite imagery of the city of Tehran, Iran with 28.5m spatial resolution to simulate the urban growth from 1988 to 2010. Statistical comparison of the ground truth and the simulated image using a kappa coefficient shows an accuracy of 83.42% in comparison to the 81.13% accuracy for the conventional GeoCA model. Moreover, decreasing the spatial resolution by a factor of onefourth has reduced the accuracy of the HCA and GeoCA models by 1.19% and 3.04%, respectively, which shows the lower scale sensitivity of the proposed model. The HCA model is developed to have the simplicity of cellular structure together with optimum features of vector models.
Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization
Niu, Liyong; Zhang, Di
20150101
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
Designing a mirrored Howland circuit with a particle swarm optimisation algorithm
NASA Astrophysics Data System (ADS)
BertemesFilho, Pedro; Negri, Lucas H.; Vincence, Volney C.
20160601
Electrical impedance spectroscopy usually requires a wide bandwidth current source with high output impedance. Nonidealities of the operational amplifier (opamp) degrade its performance. This work presents a particle swarm algorithm for extracting the main AC characteristics of the opamp 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 openloop gain and the input and output resistance of the opamp which best fit the performance requirements of the circuit. It was also shown that the higher the openloop 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.
Particle swarm optimization algorithm for optimizing assignment of blood in blood banking system.
Olusanya, Micheal O; Arasomwan, Martins A; Adewumi, Aderemi O
20150101
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 PSObased solution to address this challenge. Simulation is based on sets of randomly generated data that mimic realworld 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 reallife deployment.
A frozen Gaussian approximationbased multilevel particle swarm optimization for seismic inversion
Li, Jinglai; Lin, Guang; Yang, Xu
20150901
In this paper, we propose a frozen Gaussian approximation (FGA)based multilevel particle swarm optimization (MLPSO) method for seismic inversion of highfrequency wave data. The method addresses two challenges in it: First, the optimization problem is highly nonconvex, which makes hard for gradientbased methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of highfrequency 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 highfrequency 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 twodimensional fullwaveform inversion example of the smoothed Marmousi model.
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
20150401
The purpose of this paper is to determine the lumped suspension parameters that minimise a multiobjective 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 [ISO2631: guide for evaluation of human exposure to wholebody 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 multiobjective 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.
Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng
20160101
Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multiobjective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personalbest archives (cognitive memories) and globalbest archive (social memory), which are updated by the predefined nondominated archive update strategy, are simultaneously designed to preserve nondominated individuals and select personalbest positions and the globalbest position. Finally, three neighborhoods are provided to search the neighborhoods of globalbest archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP.
Huang, Song; Tian, Na; Wang, Yan; Ji, Zhicheng
20160101
Taking resource allocation into account, flexible job shop problem (FJSP) is a class of complex scheduling problem in manufacturing system. In order to utilize the machine resources rationally, multiobjective particle swarm optimization (MOPSO) integrating with variable neighborhood search is introduced to address FJSP efficiently. Firstly, the assignment rules (AL) and dispatching rules (DR) are provided to initialize the population. And then special discrete operators are designed to produce new individuals and earliest completion machine (ECM) is adopted in the disturbance operator to escape the optima. Secondly, personalbest archives (cognitive memories) and globalbest archive (social memory), which are updated by the predefined nondominated archive update strategy, are simultaneously designed to preserve nondominated individuals and select personalbest positions and the globalbest position. Finally, three neighborhoods are provided to search the neighborhoods of globalbest archive for enhancing local search ability. The proposed algorithm is evaluated by using Kacem instances and Brdata instances, and a comparison with other approaches shows the effectiveness of the proposed algorithm for FJSP. PMID:27652008
Enhancing speech recognition using improved particle swarm optimization based hidden Markov model.
Selvaraj, Lokesh; Ganesan, Balakrishnan
20140101
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 (IPHMM). 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 IPHMM 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
NASA Astrophysics Data System (ADS)
Xiang, Xingcan; Mutlu, Rahim; Alici, Gursel; Li, Weihua
20140301
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 trilayer 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.
Robotic Ushaped assembly line balancing using particle swarm optimization
NASA Astrophysics Data System (ADS)
Mukund Nilakantan, J.; Ponnambalam, S. G.
20160201
Automation in an assembly line can be achieved using robots. In robotic Ushaped assembly line balancing (RUALB), robots are assigned to workstations to perform the assembly tasks on a Ushaped assembly line. The robots are expected to perform multiple tasks, because of their capabilities. Ushaped 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 Ushaped assembly lines perform better than robotic straight assembly lines in terms of cycle time.
LEE, Chang Jun
20150101
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 singlefloor plant. However, many multifloor plants have been constructed for the last decade. Therefore, the proper algorithm handling various regulations and multifloor plant should be developed. In this study, the Mixed Integer NonLinear 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
Data processing of vertical scanning whitelight interferometry based on particle swarm optimization
NASA Astrophysics Data System (ADS)
Hu, Jie; Cui, Changcai; Huang, Hui; Ye, Ruifang
20130101
In order to precisely locate the position of zero optical path difference (ZOPD) between the measuring light beam and the reference light beam in Vertical Scanning Whitelight Interferometer and then realize accurate surface measurement, the Particle Swarm Optimization (PSO) was used to process the interferometry data captured by a CCD camera. The envelope line of series of intensities of every pixel was supposed to be approximated by a Gaussian curve first. Then its parameters were optimized to find the best Gaussian curve as well as the position of ZOPD by the PSO with an objective function which minimized the residual sum of squares between the measured data and theoretical fitting curve. Finally, the measured surface can be reconstructed according to a series of best positions of ZOPD obtained by the proposed method. The simulation data and sampled data of two standard samples with different kinds of reticles from repetitive test show that the PSO is suitable for precisely locating the ZOPD with low requirements of step sampling and a small amount of pictures. Therefore, without reducing the precision, the PSO can be used in data processing of the whitelight interferometry system with relatively low requirements for stepping hardware.
Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization.
Niu, Liyong; Zhang, Di
20150101
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.
NASA Astrophysics Data System (ADS)
Li, Yuan; Gosálvez, Miguel A.; Pal, Prem; Sato, Kazuo; Xing, Yan
20150501
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 PSOCCA 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 PSOCCA 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 PSOCCA 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.
Evaluating treatment of osteoporosis using particle swarm on a bone remodelling mathematical model.
Chen, Andy B; Zhang, Ping; Yokota, Hiroki
20131201
Bone loss in osteoporosis, commonly observed in postmenopausal women and the elderly, is caused by an imbalance in activities of boneforming osteoblasts and boneresorbing osteoclasts. To treat osteoporosis and increase bone mineral density (BMD), physical activities and drugs are often recommended. Complex systems dynamics prevent an intuitive prediction of treatment strategies, and little is known about an optimal sequence for the combinatorial use of available treatments. In this study, the authors built a mathematical model of bone remodelling and developed a treatment strategy for mechanical loading and salubrinal, a synthetic chemical agent that enhances bone formation and prevents bone resorption. The model formulated a temporal BMD change of a mouse's whole skeleton in response to ovariectomy, mechanical loading and administration of salubrinal. Particle swarm optimisation was employed to maximise a performance index (a function of BMD and treatment cost) to find an ideal sequence of treatment. The best treatment was found to start with mechanical loading followed by salubrinal. As treatment costs increased, the sequence started with no treatment and usage of salubrinal became scarce. The treatment strategy will depend on individual needs and costs, and the proposed model is expected to contribute to the development of personalised treatment strategies. PMID:24712100
Wei, Qingguo; Wei, Zhonghai
20150101
A braincomputer interface (BCI) enables people suffering from affective neurological diseases to communicate with the external world. Common spatial pattern (CSP) is an effective algorithm for feature extraction in motor imagery based BCI systems. However, many studies have proved that the performance of CSP depends heavily on the frequency band of EEG signals used for the construction of covariance matrices. The use of different frequency bands to extract signal features may lead to different classification performances, which are determined by the discriminative and complementary information they contain. In this study, the broad frequency band (830 Hz) is divided into 10 subbands of band width 4 Hz and overlapping 2 Hz. Binary particle swarm optimization (BPSO) is used to find the best subband set to improve the performance of CSP and subsequent classification. Experimental results demonstrate that the proposed method achieved an average improvement of 6.91% in crossvalidation accuracy when compared to broad band CSP.
NASA Astrophysics Data System (ADS)
Rahman, Md Ashiqur; Anwar, Sohel; Izadian, Afshin
20160301
In this paper, a gradientfree optimization technique, namely particle swarm optimization (PSO) algorithm, is utilized to identify specific parameters of the electrochemical model of a LithiumIon battery with LiCoO2 cathode chemistry. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, overdischarged battery, overcharged 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/deintercalation reaction rate at the cathode, and intercalation/deintercalation 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 LiIon battery electrochemical model parameters are within reasonable accuracy as evidenced by the experimental validation results.
Lee, Chang Jun
20150101
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 singlefloor plant. However, many multifloor plants have been constructed for the last decade. Therefore, the proper algorithm handling various regulations and multifloor plant should be developed. In this study, the Mixed Integer NonLinear 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.
Automatic Parameter Tuning for the Morpheus Vehicle Using Particle Swarm Optimization
NASA Technical Reports Server (NTRS)
Birge, B.
20130101
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.
Power and time slot allocation in cognitive relay networks using particle swarm optimization.
DerakhshanBarjoei, Pouya; Dadashzadeh, Gholamreza; Razzazi, Farbod; Razavizadeh, S Mohammad
20130101
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.
Wang, Shuihua; Ji, Genlin; Dong, Zhengchao
20130101
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 crossvalidation 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, CreutzfeldJakob disease, and cerebral toxoplasmosis. The 5folded crossvalidation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BPNN and 91.33% by RBFNN. 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
Kornelakis, Aris
20101215
Particle Swarm Optimization (PSO) is a highly efficient evolutionary optimization algorithm. In this paper a multiobjective optimization algorithm based on PSO applied to the optimal design of photovoltaic gridconnected systems (PVGCSs) is presented. The proposed methodology intends to suggest the optimal number of system devices and the optimal PV module installation details, such that the economic and environmental benefits achieved during the system's operational lifetime period are both maximized. The objective function describing the economic benefit of the proposed optimization process is the lifetime system's total net profit which is calculated according to the method of the Net Present Value (NPV). The second objective function, which corresponds to the environmental benefit, equals to the pollutant gas emissions avoided due to the use of the PVGCS. The optimization's decision variables are the optimal number of the PV modules, the PV modules optimal tilt angle, the optimal placement of the PV modules within the available installation area and the optimal distribution of the PV modules among the DC/AC converters. (author)
Evacuation dynamic and exit optimization of a supermarket based on particle swarm optimization
NASA Astrophysics Data System (ADS)
Li, Lin; Yu, Zhonghai; Chen, Yang
20141201
A modified particle swarm optimization algorithm is proposed in this paper to investigate the dynamic of pedestrian evacuation from a fire in a public buildinga supermarket with multiple exits and configurations of counters. Two distinctive evacuation behaviours featured by the shortestpath strategy and the followingup 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 twophase 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.
Power and Time Slot Allocation in Cognitive Relay Networks Using Particle Swarm Optimization
Dadashzadeh, Gholamreza; Razzazi, Farbod; Razavizadeh, S. Mohammad
20130101
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
Cheung, Ngaam J; Shen, HongBin
20141101
The stable conformation of a molecule is greatly important to uncover the secret of its properties and functions. Generally, the conformation of a molecule will be the most stable when it is of the minimum potential energy. Accordingly, the determination of the conformation can be solved in the optimization framework. It is, however, not an easy task to achieve the only conformation with the lowest energy among all the potential ones because of the high complexity of the energy landscape and the exponential computation increasing with molecular size. In this paper, we develop a hierarchical and heterogeneous particle swarm optimizer (HHPSO) to deal with the problem in the minimization of the potential energy. The proposed method is evaluated over a scalable simplified molecular potential energy function with up to 200 degrees of freedom and a realistic energy function of pseudoethane molecule. The experimental results are compared with other six PSO variants and four genetic algorithms. The results show HHPSO is significantly better than the compared PSOs with pvalue less than 0.01277 over molecular potential energy function.
Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model
Selvaraj, Lokesh; Ganesan, Balakrishnan
20140101
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 (IPHMM). 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 IPHMM 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
Tan, Weng Chun; Mat Isa, Nor Ashidi
20160101
In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a preprocessing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four stateoftheart segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm. PMID:27632581
Qazi, Abroon Jamal; de Silva, Clarence W; Khan, Afzal; Khan, Muhammad Tahir
20140101
This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semiactive 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 semiactive 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 semiactive 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.
Subbulakshmi, C. V.; Deepa, S. N.
20150101
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 selfregulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent offline learning method, ELM is a singlehidden 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
NASA Astrophysics Data System (ADS)
Palma, Giuseppe; Bia, Pietro; Mescia, Luciano; Yano, Tetsuji; Nazabal, Virginie; Taguchi, Jun; Moréac, Alain; Prudenzano, Francesco
20140701
A midIR amplifier consisting of a tapered chalcogenide fiber coupled to an Erdoped chalcogenide microsphere has been optimized via a particle swarm optimization (PSO) approach. More precisely, a dedicated threedimensional 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 ionion interactions. The PSO has allowed the optimal choice of the microsphere and fiber radius, taper angle, and fibermicrosphere gap in order to maximize the amplifier gain. The taper angle and the fibermicrosphere 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 rareearthdoped 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 PSObased optimization approach has allowed the design of a microspherebased 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.
Qazi, Abroon Jamal; de Silva, Clarence W.
20140101
This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semiactive 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 semiactive 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 semiactive 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
Lin, Yuqun
20140101
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
Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization.
Niu, Liyong; Zhang, Di
20150101
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
Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm.
Naebi, Mohammad; Saberi, Eshaghali; Risbaf Fakour, Sirous; Naebi, Ahmad; Hosseini Tabatabaei, Somayeh; Ansari Moghadam, Somayeh; Bozorgmehr, Elham; Davtalab Behnam, Nasim; Azimi, Hamidreza
20160101
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
NASA Astrophysics Data System (ADS)
Zhang, Lei; Wang, Zhenzhan; Shi, Hanqing; Long, Zhiyong; Du, Huadong
20160801
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 s1 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 AMSRE 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.
Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu
20160815
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 decisionmaking 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 multiobjective 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. Multiobjective Particle Swarm Algorithm is proposed to solve the model, and the decisionmakers 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 decisionmakers/stakeholders to make decision. PMID:27110990
Tan, Weng Chun; Mat Isa, Nor Ashidi
20160101
In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a preprocessing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four stateoftheart segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm. PMID:27632581
Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie
20150101
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 ANNEvolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.
Adaptive impedance control of a hydraulic suspension system using particle swarm optimisation
NASA Astrophysics Data System (ADS)
Fateh, Mohammad Mehdi; Moradi Zirkohi, Majid
20111201
This paper presents a novel active control approach for a hydraulic suspension system subject to road disturbances. A novel impedance model is used as a model reference in a particular robust adaptive control which is applied for the first time to the hydraulic suspension system. A scheme is introduced for selecting the impedance parameters. The impedance model prescribes a desired behaviour of the active suspension system in a wide range of different road conditions. Moreover, performance of the control system is improved by applying a particle swarm optimisation algorithm for optimising control design parameters. Design of the control system consists of two interior loops. The inner loop is a force control of the hydraulic actuator, while the outer loop is a robust model reference adaptive control (MRAC). This type of MRAC has been applied for uncertain linear systems. As another novelty, despite nonlinearity of the hydraulic actuator, the suspension system and the force loop together are presented as an uncertain linear system to the MRAC. The proposed control method is simulated on a quartercar model. Simulation results show effectiveness of the method.
Motion generation of peristaltic mobile robot with particle swarm optimization algorithm
NASA Astrophysics Data System (ADS)
Homma, Takahiro; Kamamichi, Norihiro
20150301
In developments of robots, biomimetics 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 biomimetics in robotics such as legged robots, flapping robots, insecttype robots, fishtype 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 seriesconnected multimass model. Simple periodic patterns which mimic the motions of earthworms are applied in an openloop 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 metaheuristic optimization, is applied. The optimized results are investigated by comparing to simple periodic patterns.
An Accelerated Particle Swarm Optimization Algorithm on Parametric Optimization of WEDM of DieSteel
NASA Astrophysics Data System (ADS)
Muthukumar, V.; Suresh Babu, A.; Venkatasamy, R.; Senthil Kumar, N.
20150101
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 diesteel. Four machining parameters that are optimized using APSO algorithm include Pulse ontime, Pulse offtime, 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.
Chou, ShengKai; Jiau, MingKai; Huang, ShihChia
20160801
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 systemwide 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 setbased 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 setbased PSO (SPSO) can be realized by local exploration. In the simulation and experiments, two kind of discrete PSOsSPSO and binary PSO (BPSO)and a genetic algorithm (GA) are compared and examined using tested benchmarks that simulate a realworld metropolis. We observed that the SPSO 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.
Particle Swarm Optimization of LowThrust, GeocentrictoHaloOrbit 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 highthrust, chemical propulsion. Due to the increasing availability of lowthrust (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 lowthrust trajectory to take, only one is optimal. The traditional approach to spacecraft trajectory optimization utilizes some form of gradientbased 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 lowthrust transfer trajectory from a geocentric orbit to an EarthMoon, 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 lowthrust trajectory. Examples of these optimized trajectories are presented and contrasted with those found using traditional, gradientbased approaches. In summary, the results of this dissertation find
Particle creation from the quantum stress tensor
NASA Astrophysics Data System (ADS)
Firouzjaee, Javad T.; Ellis, George F. R.
20150501
Among the different methods to derive particle creation, finding the quantum stress tensor expectation value gives a covariant quantity which can be used for examining the backreaction issue. However this tensor also includes vacuum polarization in a way that depends on the vacuum chosen. Here we review different aspects of particle creation by looking at energy conservation and at the quantum stress tensor. We show that in the case of general spherically symmetric black holes that have a dynamical horizon, as occurs in a cosmological context, one cannot have pair creation on the horizon because this violates energy conservation. This confirms the results obtained in other ways in a previous paper [J. T. Firouzjaee and G. F. R. Ellis, Gen. Relativ. Gravit. 47, 6 (2015)]. Looking at the expectation value of the quantum stress tensor with three different definitions of the vacuum state, we study the nature of particle creation and vacuum polarization in black hole and cosmological models, and the associated stressenergy tensors. We show that the thermal temperature that is calculated from the particle flux given by the quantum stress tensor is compatible with the temperature determined by the affine null parameter approach. Finally, we show that in the spherically symmetric dynamic case, we can neglect the backscattering term and only consider the swave term near the future apparent horizon.
NASA Astrophysics Data System (ADS)
Fan, ShuKai S.; Chang, JuMing
20100501
This article presents a novel parallel multiswarm optimization (PMSO) algorithm with the aim of enhancing the search ability of standard singleswarm PSOs for global optimization of very largescale multimodal functions. Different from the existing multiswarm 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 masterslave 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 stateoftheart algorithms (CTSS and CLPSO) in terms of various unrotated and rotated benchmark functions taken from the literature. In the second part, the proposed multiswarm algorithm is tested on largescale multimodal benchmark functions up to 300 dimensions. The results of the PMSO algorithm show great promise in solving highdimensional problems.
Quantum Random Walks with General Particle States
NASA Astrophysics Data System (ADS)
Belton, Alexander C. R.
20140601
A convergence theorem is obtained for quantum random walks with particles in an arbitrary normal state. This unifies and extends previous work on repeatedinteractions models, including that of Attal and Pautrat (Ann Henri Poincaré 7:59104 2006) and Belton (J Lond Math Soc 81:412434, 2010; Commun Math Phys 300:317329, 2010). When the randomwalk generator acts by ampliation and either multiplication or conjugation by a unitary operator, it is shown that the quantum stochastic cocycle which arises in the limit is driven by a unitary process.
NASA Astrophysics Data System (ADS)
Li, X.; Li, S. W.
20120701
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 multiintersection 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
Efficient droplet router for digital microfluidic biochip using particle swarm optimizer
NASA Astrophysics Data System (ADS)
Pan, Indrajit; Samanta, Tuhina
20130101
Digital Microfluidic Biochip has emerged as a revolutionary finding in the field of microelectromechanical research. Different complex bioassays and pathological analysis are being efficiently performed on this miniaturized chip with negligible amount of sample specimens. Initially biochip was invented on continuousfluidflow mechanism but later it has evolved with more efficient concept of digitalfluidflow. These second generation biochips are capable of serving more complex bioassays. This operational change in biochip technology emerged with the requirement of high end computer aided design needs for physical design automation. The change also paved new avenues of research to assist the proficient design automation. Droplet routing is one of those major aspects where it necessarily requires minimization of both routing completion time and total electrode usage. This task involves optimization of multiple associated parameters. In this paper we have proposed a particle swarm optimization based approach for droplet outing. The process mainly operates in two phases where initially we perform clustering of state space and classification of nets into designated clusters. This helps us to reduce solution space by redefining local sub optimal target in the interleaved space between source and global target of a net. In the next phase we resolve the concurrent routing issues of every sub optimal situation to generate final routing schedule. The method was applied on some standard test benches and hard test sets. Comparative analysis of experimental results shows good improvement on the aspect of unit cell usage, routing completion time and execution time over some well existing methods.
NASA Astrophysics Data System (ADS)
Ghaffari Razin, Mir Reza; Voosoghi, Behzad
20161101
Wavelet neural networks (WNNs) are a new class of neural networks (NNs) that has been developed using a combined method of multilayer artificial neural networks and wavelet analysis (WA). In this paper, WNNs is used for modeling and prediction of total electron content (TEC) of ionosphere with high spatial and temporal resolution. Generally, backpropagation (BP) algorithm is used to train the neural network. While this algorithm proves to be very effective and robust in training many types of network structures, it suffers from certain disadvantages such as easy entrapment in a local minimum and slow convergence. To improve the performance of WNN in training step, the adjustment of network weights using particle swarm optimization (PSO) was proposed. The results obtained in this paper were compared with standard NN (SNN) by BP training algorithm (SNNBP), SNN by PSO training algorithm (SNNPSO) and WNN by BP training algorithm (WNNBP). For numerical experiments, observations collected at 36 GPS stations in 5 days of 2012 from Iranian permanent GPS network (IPGN) are used. The average minimum relative errors in 5 test stations for WNNPSO, WNNBP, SNNBP and SNNPSO compared with GPS TEC are 10.59%, 12.85%, 13.18%, 13.75% and average maximum relative errors are 14.70%, 17.30%, 18.53% and 20.83%, respectively. Comparison of diurnal predicted TEC values from the WNNPSO, SNNBP, SNNPSO and WNNBP models with GPS TEC revealed that the WNNPSO provides more accurate predictions than the other methods in the test area.
Diesel Engine performance improvement in a 1D engine model using Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Karra, Prashanth
20151201
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 4stroke 4cylinder GTPower based 1D diesel engine model. To achieve the multiobjective 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 nonroad 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 4050 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.
Application of particle swarm optimization to parameter search in dynamical systems
NASA Astrophysics Data System (ADS)
Matsushita, Haruna; Saito, Toshimichi
This paper proposes an application of the particle swarm optimization (PSO) to analysis of switched dynamical systems (SDS). This is the first application of PSO to bifurcation analysis. We consider the application to an example of the SDS which relates to a simplified model of photovoltaic systems such that the input is a single solar cell and is converted to the output via a boost converter. Our SDS includes a piecewise linear currentcontrolled voltage source that is a simplified model of the solar cell and the switching rule is a variant of peakcurrentcontrolled switching. We derive two equations that give perioddoubling bifurcation set and the maximum power point (MPP) for the parameter: they are objective of the analysis. The two equations are transformed into an multi objective problem (MOP) described by the hybrid fitness function consisting of two functions evaluating the validity of parameters and criteria. The proposed method permits increase (deteriorate) of some component below the criterion and the increase can help to exclude the bad component. This criteria effect helps an improvement of tradeoff problems in existing MOP solvers. Furthermore, by using the piecewise exact solution and return map for the simulation, the MOP is described exactly and the PSO can find the precise (approximate) solution. From simulation results, we confirm that the PSO for the MOP can easily find the solution parameters although a standard numerical calculation needs huge calculation amount. The efficiency of the proposed algorithm is confirmed by measuring in terms of accuracy, computation amount and robustness.
Particle swarm optimization on low dimensional pose manifolds for monocular human pose estimation
NASA Astrophysics Data System (ADS)
Brauer, Jürgen; Hübner, Wolfgang; Arens, Michael
20131001
Automatic assessment of situations with modern security and surveillance systems requires sophisticated discrimination capabilities. Therefore, action recognition, e.g. in terms of personperson or personobject 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 highdimensional 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 lowdimensional 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
Yılmaz, Ersen; Kılıkçıer, Cağlar
20130101
We use least squares support vector machine (LSSVM) utilizing a binary decision tree for classification of cardiotocogram to determine the fetal state. The parameters of LSSVM are optimized by particle swarm optimization. The robustness of the method is examined by running 10fold crossvalidation. The performance of the method is evaluated in terms of overall classification accuracy. Additionally, receiver operation characteristic analysis and cobweb representation are presented in order to analyze and visualize the performance of the method. Experimental results demonstrate that the proposed method achieves a remarkable classification accuracy rate of 91.62%.
NASA Astrophysics Data System (ADS)
SueAnn, Goh; Ponnambalam, S. G.
This paper focuses on the operational issues of a Twoechelon SingleVendorMultipleBuyers 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.
NASA Astrophysics Data System (ADS)
Verma, Harish Kumar; Jain, Cheshta
20160901
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.
Higherdimensional oscillations of quantum particles
NASA Astrophysics Data System (ADS)
Hedin, Eric
20130401
A theoretical framework is developed in which elementary particles have a component of their wave function extending into higher spatial dimensions, based on an extension of the Schr"odinger equation to include 4^th and 5^th spatial components [E. R. Hedin, Physics Essays 25, 2 (2012)]. A higherdimensional harmonic oscillator confining potential localizes particles into 3d space (characterizing the ``brane tension'' which confines Standard Model particles to the submanifold). Several consistency checks of this model are: a match with the quantum phenomenon of ``zitterbewegung''; the predicted intrinsic spin angular momentum is of order h/2π; the magnetic moment of the electron is determined (with a gyromagnetic ratio of 2); the nuclear force ``hard core'' radius is accurately predicted; the ratio of quark masses (of the up and down quarks) is found to be consistent with QCD theory; and possible explanations of the Planck mass and Planck length. An application of higherdimensional particle effects to the astrophysics of stars shows that radical physical inconsistencies are not evident. Finally, this model suggests a possible explanation of dark matter as the fractional probability manifestations of a ladder of the higherdimensional symmetric excited states of ordinary particles.
Chen, Qiang; Chen, Yunhao; Jiang, Weiguo
20160730
In the field of multiple features ObjectBased Change Detection (OBCD) for veryhighresolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through objectbased 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 objectbased hybrid multivariate alternative detection model. Two experiment cases on Worldview2/3 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 GPSObased feature selection algorithm.
NASA Astrophysics Data System (ADS)
Li, Linyi; Chen, Yun; Yu, Xin; Liu, Rui; Huang, Chang
20150301
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 subpixel mapping. In this paper, a novel discrete particle swarm optimization (DPSO) based subpixel flood inundation mapping (DPSOSFIM) method is proposed to achieve an improved accuracy in mapping inundation at a subpixel scale. The evaluation criterion for subpixel inundation mapping is formulated. The DPSOSFIM algorithm is developed, including particle discrete encoding, fitness function designing and swarm search strategy. The accuracy of DPSOSFIM in mapping inundation at a subpixel scale was evaluated using Landsat ETM + images from study areas in Australia and China. The results show that DPSOSFIM consistently outperformed the four traditional SFIM methods in these study areas. A sensitivity analysis of DPSOSFIM was also carried out to evaluate its performances. It is hoped that the results of this study will enhance the application of mediumlow spatial resolution images in inundation detection and mapping, and thereby support the ecological and environmental studies of river basins.
Li, Xuejun; Xu, Jia; Yang, Yun
20150101
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, marketoriented business model is one of the most prominent factors. The optimization of tasklevel 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 tasklevel 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
Chen, Qiang; Chen, Yunhao; Jiang, Weiguo
20160101
In the field of multiple features ObjectBased Change Detection (OBCD) for veryhighresolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through objectbased 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 objectbased hybrid multivariate alternative detection model. Two experiment cases on Worldview2/3 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 GPSObased feature selection algorithm. PMID:27483285
Chen, Qiang; Chen, Yunhao; Jiang, Weiguo
20160101
In the field of multiple features ObjectBased Change Detection (OBCD) for veryhighresolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through objectbased 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 objectbased hybrid multivariate alternative detection model. Two experiment cases on Worldview2/3 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 GPSObased feature selection algorithm. PMID:27483285
Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali
20150101
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, Melfrequency 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.
Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali
20150101
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, Melfrequency 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
NASA Astrophysics Data System (ADS)
Lahmiri, Salim
20160201
Multiresolution analysis techniques including continuous wavelet transform, empirical mode decomposition, and variational mode decomposition are tested in the context of interest rate nextday 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, 3Month, 6Month and 1Year treasury bills, and effective federal fund rate. The forecasting results show that all multiresolutionbased prediction systems outperform the conventional reference models on the criteria of mean absolute error, mean absolute deviation, and root meansquared 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.
Li, Xuejun; Xu, Jia; Yang, Yun
20150101
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, marketoriented business model is one of the most prominent factors. The optimization of tasklevel 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 tasklevel 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.
NASA Astrophysics Data System (ADS)
Zhang, Xingwu; Gao, Robert X.; Yan, Ruqiang; Chen, Xuefeng; Sun, Chuang; Yang, Zhibo
20160801
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 MWFEMbased 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.
Thermal equilibrium of two quantum Brownian particles
Valente, D. M.; Caldeira, A. O.
20100115
The influence of the environment in the thermal equilibrium properties of a bipartite continuous variable quantum system is studied. The problem is treated within a systemplusreservoir approach. The considered model reproduces the Brownian motion when the two particles are isolated and induces an effective interaction between them, depending on the choice of the spectral function of the bath. The coupling between the system and the environment guarantees the translational invariance of the system in the absence of an external potential. The entanglement between the particles is measured by the logarithmic negativity, which is shown to monotonically decrease with the increase of the temperature. A range of finite temperatures is found in which entanglement is still induced by the reservoir.
Integrative modeling and novel particle swarmbased 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 underperformance 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 highperforming wind farms. The primary contribution of this research is the effective quantification of the complex combined influence of wind turbine features, turbine placement, farmland 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) turbinewind 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
Algebraic formulation of quantum theory, particle identity and entanglement
NASA Astrophysics Data System (ADS)
Govindarajan, T. R.
20160801
Quantum theory as formulated in conventional framework using statevectors in Hilbert spaces misses the statistical nature of the underlying quantum physics. Formulation using operators 𝒞∗ algebra and density matrices appropriately captures this feature in addition leading to the correct formulation of particle identity. In this framework, Hilbert space is an emergent concept. Problems related to anomalies and quantum epistemology are discussed.
Quantum Particles from Classical Probabilities in Phase Space
NASA Astrophysics Data System (ADS)
Wetterich, C.
20121001
Quantum particles in a potential are described by classical statistical probabilities. We formulate a basic time evolution law for the probability distribution of classical position and momentum such that all known quantum phenomena follow, including interference or tunneling. The appropriate quantum observables for position and momentum contain a statistical part which reflects the roughness of the probability distribution. "Zwitters" realize a continuous interpolation between quantum and classical particles. Such objects may provide for an effective oneparticle description of classical or quantum collective states as droplets of a liquid, macromolecules or a BoseEinstein condensate. They may also be used for quantitative fundamental tests of quantum mechanics. We show that the ground state for zwitters has no longer a sharp energy. This feature permits to put quantitative experimental bounds on a small parameter for possible deviations from quantum mechanics.
Quantum supremacy of manyparticle thermal machines
NASA Astrophysics Data System (ADS)
Jaramillo, J.; Beau, M.; del Campo, A.
20160701
While the emergent field of quantum thermodynamics has the potential to impact energy science, the performance of thermal machines is often classical. We ask whether quantum effects can boost the performance of a thermal machine to reach quantum supremacy, i.e., surpassing both the efficiency and power achieved in classical thermodynamics. To this end, we introduce a nonadiabatic quantum heat engine operating an Otto cycle with a manyparticle working medium, consisting of an interacting Bose gas confined in a timedependent harmonic trap. It is shown that thanks to the interplay of nonadiabatic and manyparticle quantum effects, this thermal machine can outperform an ensemble of singleparticle heat engines with same resources, demonstrating the quantum supremacy of manyparticle thermal machines.
Counting statistics of manyparticle quantum walks
Mayer, Klaus; Tichy, Malte C.; Buchleitner, Andreas; Mintert, Florian; Konrad, Thomas
20110615
We study quantum walks of many noninteracting particles on a beam splitter array as a paradigmatic testing ground for the competition of single and manyparticle interference in a multimode system. We derive a general expression for multimode particlenumber correlation functions, valid for bosons and fermions, and infer pronounced signatures of manyparticle interferences in the counting statistics.
Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour
20120901
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 5MW 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
NASA Astrophysics Data System (ADS)
Mandal, S. K.; Singh, Harshavardhan; Mahanti, G. K.; Ghatak, Rowdra
20141001
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 ontime durations of the timemodulated elements and the static amplitudes of the array elements are perturbed in such a way that the same ontime sequence and discrete values of static amplitudes for four bit digital attenuators produces either a pencil or a flattop 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 20element linear array are presented.
Jiang, Haiming; Xie, Kang; Wang, Yafei
20100524
An effective pump scheme for the design of broadband and flat gain spectrum Raman fiber amplifiers is proposed. This novel approach uses a new shooting algorithm based on a modified NewtonRaphson method and a contraction factor to solve the two point boundary problems of Raman coupled equations more stably and efficiently. In combination with an improved particle swarm optimization method, which improves the efficiency and convergence rate by introducing a new parameter called velocity acceptability probability, this scheme optimizes the wavelengths and power levels for the pumps quickly and accurately. Several broadband Raman fiber amplifiers in C+L band with optimized pump parameters are designed. An amplifier of 4 pumps is designed to deliver an average onoff gain of 13.3 dB for a bandwidth of 80 nm, with about +/0.5 dB in band maximum gain ripples.
Wang, Wei; Chen, Xiyuan
20160810
Modeling and compensation of temperature drift is an important method for improving the precision of fiberoptic 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.
Buyukada, Musa
20160901
Cocombustion 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 multilayer 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 cocombustion process. PMID:27243606
Buyukada, Musa
20160901
Cocombustion 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 multilayer 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 cocombustion process.
Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan
20160701
This paper proposes an epilepsy detection and closedloop 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 excitatoryinhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportionintegration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the modelbased early seizure detection and closedloop control treatment design. PMID:27475078
Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour
20120901
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 5MW 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.
NASA Astrophysics Data System (ADS)
Vaz, Miguel; Luersen, Marco A.; MuñozRojas, Pablo A.; Trentin, Robson G.
20160401
Application of optimization techniques to the identification of inelastic material parameters has substantially increased in recent years. The complex stressstrain 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 gradientbased Sequential Quadratic Programming method, of the NelderMead downhill simplex algorithm, of Particle Swarm Optimization (PSO), and of a globallocal PSONelderMead 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 NelderMead algorithm to obtain the minimum itself.
NASA Astrophysics Data System (ADS)
Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan
20160701
This paper proposes an epilepsy detection and closedloop 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 excitatoryinhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportionintegration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the modelbased early seizure detection and closedloop control treatment design.
Master Equation for a Quantum Particle in a Gas
Hornberger, Klaus
20060811
The equation for the quantum motion of a Brownian particle in a gaseous environment is derived by means of Smatrix theory. This quantum version of the linear Boltzmann equation accounts nonperturbatively for the quantum effects of the scattering dynamics and describes decoherence and dissipation in a unified framework. As a completely positive master equation it incorporates both the known equation for an infinitely massive Brownian particle and the classical linear Boltzmann equation as limiting cases.
Master equation for a quantum particle in a gas.
Hornberger, Klaus
20060811
The equation for the quantum motion of a Brownian particle in a gaseous environment is derived by means of Smatrix theory. This quantum version of the linear Boltzmann equation accounts nonperturbatively for the quantum effects of the scattering dynamics and describes decoherence and dissipation in a unified framework. As a completely positive master equation it incorporates both the known equation for an infinitely massive Brownian particle and the classical linear Boltzmann equation as limiting cases.
Han, Min; Fan, Jianchao; Wang, Jun
20110901
A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive timedelay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays. PMID:21803682
Quantum backreaction through the Bohmian particle.
Prezhdo, O V; Brooksby, C
20010401
A novel solution to the quantum backreaction problem in a mixed quantumclassical simulation is provided using the Bohmian interpretation of quantum mechanics. The Bohmian backreaction is unique, computationally simple, features reaction channel branching, and easily gives the full classical limit. The Bohmian quantumclassical method is illustrated by application to a model of O2 interacting with a Pt surface.
On the photoelectric quantum yield of small dust particles
NASA Astrophysics Data System (ADS)
Kimura, Hiroshi
20160701
Photoelectron emission is crucial to electric charging of dust particles around mainsequence stars and gas heating in various dusty environments. An estimate of the photoelectric processes contains an illdefined parameter called the photoelectric quantum yield, which is the total number of electrons ejected from a dust particle per absorbed photon. Here we revisit the socalled small particle effect of photoelectron emission and provide an analytical model to estimate photoelectric quantum yields of small dust particles in sizes down to nanometers. We show that the small particle effect elevates the photoelectric quantum yields of nanoparticles up to by a factor of 103 for carbon, water ice, and organics, and a factor of 102 for silicate, silicon carbide, and iron. We conclude the surface curvature of the particles is a quantity of great importance to the small particle effect, unless the particles are submicrometers in radius or larger.
Kholodtsova, Maria N; Daul, Christian; Loschenov, Victor B; Blondel, Walter C P M
20160613
This paper presents a new approach to estimate optical properties (absorption and scattering coefficients µa and µs) of biological tissues from spatiallyresolved spectroscopy measurements. A Particle Swarm Optimization (PSO)based algorithm was implemented and firstly modified to deal with spatial and spectral resolutions of the data, and to solve the corresponding inverse problem. Secondly, the optimization was improved by fitting exponential decays to the two best points among all clusters of the "particles" randomly distributed all over the parameter space (µs, µa) of possible solutions. The consequent acceleration of all the groups of particles to the "best" curve leads to significant error decrease in the optical property estimation. The study analyzes the estimated optical property error as a function of the various PSO parameter combinations, and several performance criteria such as the costfunction error and the number of iterations in the algorithms proposed. The final one led to error values between ground truth and estimated values of µs and µa less than 6%. PMID:27410289
Quantum correlations of identical particles subject to classical environmental noise
NASA Astrophysics Data System (ADS)
Beggi, Andrea; Buscemi, Fabrizio; Bordone, Paolo
20160901
In this work, we propose a measure for the quantum discord of indistinguishable particles, based on the definition of entanglement of particles given in Wiseman and Vaccaro (Phys Rev Lett 91:097902, 2003. doi: 10.1103/PhysRevLett.91.097902). This discord of particles is then used to evaluate the quantum correlations in a system of two identical bosons (fermions), where the particles perform a quantum random walk described by the Hubbard Hamiltonian in a 1D lattice. The dynamics of the particles is either unperturbed or subject to a classical environmental noise—such as random telegraph, pink or brown noise. The observed results are consistent with those for the entanglement of particles, and we observe that onsite interaction between particles have an important protective effect on correlations against the decoherence of the system.
Quantum correlations of identical particles subject to classical environmental noise
NASA Astrophysics Data System (ADS)
Beggi, Andrea; Buscemi, Fabrizio; Bordone, Paolo
20160601
In this work, we propose a measure for the quantum discord of indistinguishable particles, based on the definition of entanglement of particles given in Wiseman and Vaccaro (Phys Rev Lett 91:097902, 2003. doi: 10.1103/PhysRevLett.91.097902). This discord of particles is then used to evaluate the quantum correlations in a system of two identical bosons (fermions), where the particles perform a quantum random walk described by the Hubbard Hamiltonian in a 1D lattice. The dynamics of the particles is either unperturbed or subject to a classical environmental noise—such as random telegraph, pink or brown noise. The observed results are consistent with those for the entanglement of particles, and we observe that onsite interaction between particles have an important protective effect on correlations against the decoherence of the system.
Coherent States of Quantum Free Particle on the Spherical Space
NASA Astrophysics Data System (ADS)
Dehdashti, Shahram; Roknizadeh, Rasoul; Mahdifar, Ali; Chen, Hongsheng
20160101
In this paper, we study the quantum free particle on the spherical space by applying da costa approach for quantum particle on the curved space. We obtain the discrete energy eigenvalues and associated normalized eigenfunctions of the free particle on the sphere. In addition, we introduce the GazeauKlauder coherent states of free particle on the sphere. Then, the Gaussian coherent states is defined, which is used to describe the localized particle on the spherical space. Finally, we study the relation between the fdeformed coherent states and GazeauKlauder ones for this system.
SUET259: Particle Swarm Optimization in Radial Dose Function Fitting for a Novel Iodine125 Seed
Wu, X; Duan, J; Popple, R; Huang, M; Shen, S; Brezovich, I; Cardan, R; Benhabib, S
20140601
Purpose: To determine the coefficients of bi and triexponential functions for the best fit of radial dose functions of the new iodine brachytherapy source: Iodine125 Seed AgX100. Methods: The particle swarm optimization (PSO) method was used to search for the coefficients of the biand triexponential 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 bestknown 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 I125 seed AgX100 considered as a point source, the maximum deviation from the published data is less than 2.9% for biexponential fitting function and 0.2% for triexponential fitting function. For its line source, the maximum deviation is less than 1.1% for biexponential fitting function and 0.08% for triexponential fitting function. Conclusion: PSO is a powerful method in searching coefficients for biexponential and triexponential fitting functions. The bi and triexponential models of Iodine125 seed AgX100 point and line sources obtained with PSO optimization provide accurate analytical forms of the radial dose function. The triexponential fitting function is more accurate than the biexponential function.
Quantum effects for particles channeling in a bent crystal
NASA Astrophysics Data System (ADS)
Feranchuk, Ilya; San, Nguyen Quang
20160901
Quantum mechanical theory for channeling of the relativistic charged particles in the bent crystals is considered in the paper. Quantum effects of underbarrier tunneling are essential when the radius of the curvature is closed to its critical value. In this case the wave functions of the quasistationary states corresponding to the particles captured in a channel are presented in the analytical form. The efficiency of channeling of the particles and their angular distribution at the exit crystal surface are calculated. Characteristic experimental parameters for observation the quantum effects are estimated.
NASA Astrophysics Data System (ADS)
Şahin, Erol; Girgin, Sertan; Bayindir, Levent; Turgut, Ali Emre
Swarm robotics is a novel approach to the coordination of large numbers of robots and has emerged as the application of swarm intelligence to multirobot systems. Different from other swarm intelligence studies, swarm robotics puts emphases on the physical embodiment of individuals and realistic interactions among the individuals and between the individuals and the environment. In this chapter, we present a brief review of this new approach. We first present its definition, discuss the main motivations behind the approach, as well as its distinguishing characteristics and major coordination mechanisms. Then we present a brief review of swarm robotics research along four axes; namely design, modelling and analysis, robots and problems.
Li, B; Robson, R E; White, R D
20060801
The effect of a magnetic field at right angles to an electric field on spatial relaxation of a swarm of charged particles emitted by a plane source into a gasthe idealized steadystate Townsend experimentis examined. The Boltzmann equation is solved using an adaptation of the "twotemperature" moment method, involving a Burnett function representation of the velocity distribution function, a technique which is valid for charged particles of arbitrary mass and is intrinsically of a "multiterm" nature. Results are presented for electrons in model and real gases, and are benchmarked against an exact analytical solution of the Boltzmann equation for a particular collision model. The application of a magnetic field significantly alters the relaxation profiles: in general, it can both enhance or retard spatial relaxation of transport properties. For methane gas, a multiterm analysis is essential to correctly account for the relaxation near the source, even though a twoterm approximation may be sufficient when the magnetic field is sufficiently strong and hydrodynamic conditions dominate.
Agrawal, Shikha; Silakari, Sanjay; Agrawal, Jitendra
20151101
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 1,2,3trichloro1flouropropane 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
Cakar, Tarik; Koker, Rasit
20150101
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 hybridPSO system. NDR checked sequential jobs according to total weighted tardiness factor. All system is named as neurohybridPSO solution system. PMID:26221134
Agrawal, Shikha; Silakari, Sanjay; Agrawal, Jitendra
20151101
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 1,2,3trichloro1flouropropane 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.
Inverse 4D conformal planning for lung SBRT using particle swarm optimization.
Modiri, A; Gu, X; Hagan, A; Bland, R; Iyengar, P; Timmerman, R; Sawant, A
20160821
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 intelligencebased stochastic optimization technique. Conventional lung CRTSBRT uses a 4DCT to create an internal target volume and then, using forwardplanning, generates a 3D conformal plan. In contrast, we investigate an inverseplanning 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 proofofconcept, five nonsmallcell 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
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.
20160801
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 intelligencebased stochastic optimization technique. Conventional lung CRTSBRT uses a 4DCT to create an internal target volume and then, using forwardplanning, generates a 3D conformal plan. In contrast, we investigate an inverseplanning 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 proofofconcept, five nonsmallcell 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.
Inverse 4D conformal planning for lung SBRT using particle swarm optimization.
Modiri, A; Gu, X; Hagan, A; Bland, R; Iyengar, P; Timmerman, R; Sawant, A
20160821
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 intelligencebased stochastic optimization technique. Conventional lung CRTSBRT uses a 4DCT to create an internal target volume and then, using forwardplanning, generates a 3D conformal plan. In contrast, we investigate an inverseplanning 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 proofofconcept, five nonsmallcell 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.
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.
20160801
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 intelligencebased stochastic optimization technique. Conventional lung CRTSBRT uses a 4DCT to create an internal target volume and then, using forwardplanning, generates a 3D conformal plan. In contrast, we investigate an inverseplanning 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 proofofconcept, five nonsmallcell 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.
Synchronizing quantum and classical clocks made of quantum particles
NASA Astrophysics Data System (ADS)
Flores, Philip Caesar M.; Caballar, Roland Cristopher F.; Galapon, Eric A.
20160901
We demonstrate that the quantum corrections to the classical arrival time for a quantum object in a potential free region of space, as computed in Phys. Rev. A 80, 030102(R) (2009), 10.1103/PhysRevA.80.030102, can be eliminated up to a given order of ℏ by choosing an appropriate positiondependent phase for the object's wave function. This then implies that we can make the quantum arrival time of the object as close as possible to its corresponding classical arrival time, allowing us to synchronize a classical and quantum clock, which tells time using the classical and quantum arrival time of the object, respectively. We provide an example for synchronizing such a clock by making use of a quantum object with a positiondependent phase imprinted on the object's initial wave function with the use of an impulsive potential.
Hausdorff dimension of a particle path in a quantum manifold
Nicolini, Piero; Niedner, Benjamin
20110115
After recalling the concept of the Hausdorff dimension, we study the fractal properties of a quantum particle path. As a novelty we consider the possibility for the space where the particle propagates to be endowed with a quantumgravityinduced minimal length. We show that the Hausdorff dimension accounts for both the quantum mechanics uncertainty and manifold fluctuations. In addition the presence of a minimal length breaks the selfsimilarity property of the erratic path of the quantum particle. Finally we establish a universal property of the Hausdorff dimension as well as the spectral dimension: They both depend on the amount of resolution loss which affects both the path and the manifold when quantum gravity fluctuations occur.
Particles, Waves, and the Interpretation of Quantum Mechanics
ERIC Educational Resources Information Center
Christoudouleas, N. D.
19750101
Presents an explanation, without mathematical equations, of the basic principles of quantum mechanics. Includes waveparticle duality, the probability character of the wavefunction, and the uncertainty relations. (MLH)
Particle statistics affects quantum decay and Fano interference.
Crespi, Andrea; Sansoni, Linda; Della Valle, Giuseppe; Ciamei, Alessio; Ramponi, Roberta; Sciarrino, Fabio; Mataloni, Paolo; Longhi, Stefano; Osellame, Roberto
20150301
Quantum mechanical decay, Fano interference, and bound states with energy in the continuum are ubiquitous phenomena in different areas of physics. Here we experimentally demonstrate that particle statistics strongly affects quantum mechanical decay in a multiparticle system. By considering propagation of twophoton states in engineered photonic lattices, we simulate quantum decay of two noninteracting particles in a multilevel FanoAnderson model. Remarkably, when the system sustains a bound state in the continuum, fractional decay is observed for bosonic particles, but not for fermionic ones. Complete decay in the fermionic case arises because of the Pauli exclusion principle, which forbids the bound state to be occupied by the two fermions. Our experiment indicates that particle statistics can tune manybody quantum decay from fractional to complete.
NASA Astrophysics Data System (ADS)
Polprasert, Jirawadee; Ongsakul, Weerakorn; Dieu, Vo Ngoc
20110601
This paper proposes a selforganizing hierarchical particle swarm optimization (SPSO) with timevarying acceleration coefficients (TVAC) for solving economic dispatch (ED) problem with nonsmooth functions including multiple fuel options (MFO) and valvepoint 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 reinitialization 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 nonsmooth 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 nonsmooth ED problems than many other methods.
Particle scattering in loop quantum gravity.
Modesto, Leonardo; Rovelli, Carlo
20051101
We devise a technique for defining and computing point functions in the context of a backgroundindependent gravitational quantum field theory. We construct a tentative implementation of this technique in a perturbatively finite model defined using spin foam techniques in the context of loop quantum gravity.
From Waves to Particle Tracks and Quantum Probabilities
NASA Astrophysics Data System (ADS)
Falkenburg, Brigitte
Here, the measurement methods for identifying massive charged particles are investigated. They have been used from early cosmic ray studies up to the present day. Laws such as the classical Lorentz force and Einstein's relativistic kinematics were established before the rise of quantum mechanics. Later, it became crucial to measure the energy loss of charged particles in matter. In 1930, Bethe developed a semiclassical model based on the quantum mechanics of scattering. In the early 1930s, he and others calculated the passage of charged particles through matter including pair creation and bremsstrahlung. Due to missing trust in quantum electrodynamics, however, only semiempirical methods were employed in order to estimate the mass and charge from the features of particle tracks. In 1932, Anderson inserted a lead plate into the cloud chamber in order to determine the flight direction and charge of the `positive electron'. In the 1940s, nuclear emulsions helped to resolve puzzles about particle identification and quantum electrodynamics. Later, the measurement theory was extended in a cumulative process by adding conservation laws for dynamic properties, probabilistic quantum formulas for resonances, scattering cross sections, etc. The measurement method was taken over from cosmic ray studies to the era of particle accelerators, and finally taken back from there to astroparticle physics. The measurement methods remained the same, but in the transition from particle to astroparticle physics the focus of interest shifted. Indeed, the experimental methods of both fields explore the grounds of `new physics' in complementary ways.
NASA Astrophysics Data System (ADS)
Campbell, Glenn; Phinn, Stuart R.
20090901
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 HydrolightÂ® 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 airwater 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.
Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei
20160101
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 (MPPSO) is proposed. In this method, the rangebased 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 rangebased 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
Chiang, TzuAn; Che, Z H; Cui, Zhihua
20140101
This study designed a crossstage 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 crossstage 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
NASA Astrophysics Data System (ADS)
Prabowo, K.; Sumaryada, T.; Kartono, A.
20160101
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.
Wang, Wei; Chen, Xiyuan
20160810
Modeling and compensation of temperature drift is an important method for improving the precision of fiberoptic 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
Mousavi, Seyed Mohsen; Niaki, S. T. A.; Bahreininejad, Ardeshir; Musa, Siti Nurmaya
20140101
A multiitem multiperiod inventory control model is developed for knowndeterministic 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 multiobjective 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
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
20160101
A backpropagation (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 PSOBP 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
Shabri, Ani; Samsudin, Ruhaidah
20140101
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.
NASA Astrophysics Data System (ADS)
HosseiniBioki, M. M.; Rashidinejad, M.; Abdollahi, A.
20131101
Load shedding is a crucial issue in power systems especially under restructured electricity environment. Marketdriven load shedding in reregulated power systems associated with security as well as reliability is investigated in this paper. A technoeconomic multiobjective 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 marketdriven structure, generators offer their bidding blocks while the dispatchable loads will bid their priceresponsive demands. An independent system operator (ISO) derives a market clearing price (MCP) while rescheduling the amount of generating power in both precontingency and postcontingency conditions. The proposed methodology is developed on a 3bus system and then is applied to a modified IEEE 30bus 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.
Li, Jianzhong
20140421
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 threestep phaseshifting 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 subband of the contourlettransformed 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.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
20160101
A backpropagation (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 PSOBP 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.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
20160101
A backpropagation (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 PSOBP 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
Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei
20160101
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 (MPPSO) is proposed. In this method, the rangebased 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 rangebased 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
Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei
20160101
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 (MPPSO) is proposed. In this method, the rangebased 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 rangebased 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.
NASA Astrophysics Data System (ADS)
Yang, Yahong; Zhao, Fuqing; Hong, Yi; Yu, Dongmei
20051201
Integration of process planning with scheduling by considering the manufacturing system's capacity, cost and capacity in its workshop is a critical issue. The concurrency between them can also eliminate the redundant process and optimize the entire production cycle, but most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. In this paper, a fuzzy inference system (FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop manufacturing system is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability. The mean time to failure (MTF) values is input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalized based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of unutilization machines, sometimes faced by unreliable machine, the particle swarm optimization (PSO) have been used to balance the load for all the machines. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling.
Li, Jianzhong
20140421
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 threestep phaseshifting 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 subband of the contourlettransformed 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
NASA Astrophysics Data System (ADS)
Tran, Binh; Xue, Bing; Zhang, Mengjie; Nguyen, Su
20160701
Feature selection is an essential step in classification tasks with a large number of features, such as in gene expression data. Recent research has shown that particle swarm optimisation (PSO) is a promising approach to feature selection. However, it also has potential limitation to get stuck into local optima, especially for gene selection problems with a huge search space. Therefore, we developed a PSO algorithm (PSOLSRG) with a fast "local search" combined with a gbest resetting mechanism as a way to improve the performance of PSO for feature selection. Furthermore, since many existing PSObased feature selection approaches on the gene expression data have feature selection bias, i.e. no unseen test data is used, 2 sets of experiments on 10 gene expression datasets were designed: with and without feature selection bias. As compared to standard PSO, PSO with gbest resetting only, and PSO with local search only, PSOLSRG obtained a substantial dimensionality reduction and a significant improvement on the classification performance in both sets of experiments. PSOLSRG outperforms the other three algorithms when feature selection bias exists. When there is no feature selection bias, PSOLSRG selects the smallest number of features in all cases, but the classification performance is slightly worse in a few cases, which may be caused by the overfitting problem. This shows that feature selection bias should be avoided when designing a feature selection algorithm to ensure its generalisation ability on unseen data.
Shabri, Ani; Samsudin, Ruhaidah
20140101
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
Chiang, TzuAn; Che, Z. H.
20140101
This study designed a crossstage 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 crossstage 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
Quantum limited particle sensing in optical tweezers
Tay, J.W.; Hsu, Magnus T. L.; Bowen, Warwick P.
20091215
Particle sensing in optical tweezers systems provides information on the position, velocity, and force of the specimen particles. The conventional quadrant detection scheme is applied ubiquitously in optical tweezers experiments to quantify these parameters. In this paper, we show that quadrant detection is nonoptimal for particle sensing in optical tweezers and propose an alternative optimal particle sensing scheme based on spatial homodyne detection. A formalism for particle sensing in terms of transverse spatial modes is developed and numerical simulations of the efficacies of both quadrant and spatial homodyne detection are shown. We demonstrate that 1 order of magnitude improvement in particle sensing sensitivity can be achieved using spatial homodyne over quadrant detection.
Quantum interface to charged particles in a vacuum
NASA Astrophysics Data System (ADS)
Okamoto, Hiroshi
20151101
A superconducting qubit device suitable for interacting with a flying electron has recently been proposed [Okamoto and Nagatani, Appl. Phys. Lett. 104, 062604 (2014), 10.1063/1.4865244]. Either a clockwise or counterclockwise directed loop of half magnetic flux quantum encodes a qubit, which naturally interacts with any single charged particle with arbitrary kinetic energy. Here, the device's properties, sources of errors, and possible applications are studied in detail. In particular, applications include detection of a charged particle essentially without applying a classical force to it. Furthermore, quantum states can be transferred between an array of the proposed devices and the charged particle.
PREFACE: Particles and Fields: Classical and Quantum
NASA Astrophysics Data System (ADS)
Asorey, M.; ClementeGallardo, J.; Marmo, G.
20070701
This volume contains some of the contributions to the Conference Particles and Fields: Classical and Quantum, which was held at Jaca (Spain) in September 2006 to honour George Sudarshan on his 75th birthday. Former and current students, associates and friends came to Jaca to share a few wonderful days with George and his family and to present some contributions of their present work as influenced by George's impressive achievements. This book summarizes those scientific contributions which are presented as a modest homage to the master, collaborator and friend. At the social ceremonies various speakers were able to recall instances of his lifelong activity in India, the United States and Europe, adding colourful remarks on the friendly and intense atmosphere which surrounded those collaborations, some of which continued for several decades. This meeting would not have been possible without the financial support of several institutions. We are deeply indebted to Universidad de Zaragoza, Ministerio de Educación y Ciencia de España (CICYT), Departamento de Ciencia, Tecnología y Universidad del Gobierno de Aragón, Universitá di Napoli 'Federico II' and Istituto Nazionale di Fisica Nucleare. Finally, we would like to thank the participants, and particularly George's family, for their contribution to the wonderful atmosphere achieved during the Conference. We would like also to acknowledge the authors of the papers collected in the present volume, the members of the Scientific Committee for their guidance and support and the referees for their generous work. M Asorey, J ClementeGallardo and G Marmo The Local Organizing Committee George Sudarshan
A. Ashtekhar (Pennsylvania State University, USA)  
L. J. Boya (Universidad de Zaragoza, Spain)  
I. Cirac (Max Planck Institute, Garching
