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.
Path planning for UAV based on quantumbehaved particle swarm optimization
NASA Astrophysics Data System (ADS)
Fu, Yangguang; Ding, Mingyue; Zhou, Chengping; Cai, Chao; Sun, Yangguang
20091001
Based on quantumbehaved particle swarm optimization (QPSO), a novel path planner for unmanned aerial vehicle (UAV) is employed to generate a safe and flyable path. The standard particle swarm optimization (PSO) and quantumbehaved particle swarm optimization (QPSO) are presented and compared through a UAV path planning application. Every particle in swarm represents a potential path in search space. For the purpose of pruning the search space, constraints are incorporated into the prespecified cost function, which is used to evaluate whether a particle is good or not. As the system iterated, each particle is pulled toward its local attractor, which is located between the personal best position (pbest) and the global best position (gbest) based on the interaction of particles' individual searches and group's public search. For the sake of simplicity, we only consider planning the projection of path on the plane and assume threats are static instead of moving. Simulation results demonstrated the effectiveness and feasibility of the proposed approach.
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.
NASA Astrophysics Data System (ADS)
Liu, Tianyu; Jiao, Licheng; Ma, Wenping; Shang, Ronghua
20170301
In this paper, an improved quantumbehaved particle swarm optimization (CLQPSO), which adopts a new collaborative learning strategy to generate local attractors for particles, is proposed to solve nonlinear numerical problems. Local attractors, which directly determine the convergence behavior of particles, play an important role in quantumbehaved particle swarm optimization (QPSO). In order to get a promising and efficient local attractor for each particle, a collaborative learning strategy is introduced to generate local attractors in the proposed algorithm. Collaborative learning strategy consists of two operators, namely orthogonal operator and comparison operator. For each particle, orthogonal operator is used to discover the useful information that lies in its personal and global best positions, while comparison operator is used to enhance the particle's ability of jumping out of local optima. By using a probability parameter, the two operators cooperate with each other to generate local attractors for particles. A comprehensive comparison of CLQPSO with some stateoftheart evolutionary algorithms on nonlinear numeric optimization functions demonstrates the effectiveness of the proposed algorithm.
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.
Du, Yanqin; Huang, Hua
20111001
Fetal electrocardiogram (FECG) is an objective index of the activities of fetal cardiac electrophysiology. The acquired FECG is interfered by maternal electrocardiogram (MECG). How to extract the fetus ECG quickly and effectively has become an important research topic. During the noninvasive FECG extraction algorithms, independent component analysis(ICA) algorithm is considered as the best method, but the existing algorithms of obtaining the decomposition of the convergence properties of the matrix do not work effectively. Quantum particle swarm optimization (QPSO) is an intelligent optimization algorithm converging in the global. In order to extract the FECG signal effectively and quickly, we propose a method combining ICA and QPSO. The results show that this approach can extract the useful signal more clearly and accurately than other noninvasive methods.
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
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.
A Parallel Particle Swarm Optimizer
20030101
by a computationally demanding biomechanical system identification problem, we introduce a parallel implementation of a stochastic population based...concurrent computation. The parallelization of the Particle Swarm Optimization (PSO) algorithm is detailed and its performance and characteristics demonstrated for the biomechanical system identification problem as example.
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.
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
Incremental Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Xu, Xiaohua; Pan, Zhoujin; Xi, Yanqiu; Chen, Ling
By simulating the population size of the human evolution, a PSO algorithm with increment of particle size (IPPSO) was proposed. Without changing the PSO operations, IPPSO can obtain better solutions with less time cost by modifying the structure of traditional PSO. Experimental results show that IPPSO using logistic model is more efficient and requires less computation time than using linear function in solving more complex program problems.
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.
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
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
Liu, Long; Sun, Jun; Xu, Wenbo; Du, Guocheng; Chen, Jian
20090101
Hyaluronic acid (HA) is a natural biopolymer with unique physiochemical and biological properties and finds a wide range of applications in biomedical and cosmetic fields. It is important to increase HA production to meet the increasing HA market demand. This work is aimed to model and optimize the amino acids addition to enhance HA production of Streptococcus zooepidemicus with radial basis function (RBF) neural network coupling quantumbehaved particle swarm optimization (QPSO) algorithm. In the RBFQPSO approach, RBF neural network is used as a bioprocess modeling tool and QPSO algorithm is applied to conduct the optimization with the established RBF neural network black model as the objective function. The predicted maximum HA yield was 6.92 g/L under the following conditions: arginine 0.062 g/L, cysteine 0.036 g/L, and lysine 0.043 g/L. The optimal amino acids addition allowed HA yield increased from 5.0 g/L of the control to 6.7 g/L in the validation experiments. Moreover, the modeling and optimization capacity of the RBFQPSO approach was compared with that of response surface methodology (RSM). It was indicated that the RBFQPSO approach gave a slightly better modeling and optimization result compared with RSM. The developed RBFQPSO approach in this work may be helpful for the modeling and optimization of the other multivariable, nonlinear, timevariant bioprocesses.
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.
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.
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.
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.
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
Particle Swarm Optimization With Interswarm Interactive Learning Strategy.
Qin, Quande; Cheng, Shi; Zhang, Qingyu; Li, Li; Shi, Yuhui
20161001
The learning strategy in the canonical particle swarm optimization (PSO) algorithm is often blamed for being the primary reason for loss of diversity. Population diversity maintenance is crucial for preventing particles from being stuck into local optima. In this paper, we present an improved PSO algorithm with an interswarm interactive learning strategy (IILPSO) by overcoming the drawbacks of the canonical PSO algorithm's learning strategy. IILPSO is inspired by the phenomenon in human society that the interactive learning behavior takes place among different groups. Particles in IILPSO are divided into two swarms. The interswarm interactive learning (IIL) behavior is triggered when the best particle's fitness value of both the swarms does not improve for a certain number of iterations. According to the best particle's fitness value of each swarm, the softmax method and roulette method are used to determine the roles of the two swarms as the learning swarm and the learned swarm. In addition, the velocity mutation operator and global best vibration strategy are used to improve the algorithm's global search capability. The IIL strategy is applied to PSO with global star and local ring structures, which are termed as IILPSOG and IILPSOL algorithm, respectively. Numerical experiments are conducted to compare the proposed algorithms with eight popular PSO variants. From the experimental results, IILPSO demonstrates the good performance in terms of solution accuracy, convergence speed, and reliability. Finally, the variations of the population diversity in the entire search process provide an explanation why IILPSO performs effectively.
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
Particleswarm structure prediction on clusters
NASA Astrophysics Data System (ADS)
Lv, Jian; Wang, Yanchao; Zhu, Li; Ma, Yanming
20120801
We have developed an efficient method for cluster structure prediction based on the generalization of particle swarm optimization (PSO). A local version of PSO algorithm was implemented to utilize a fine exploration of potential energy surface for a given nonperiodic system. We have specifically devised a technique of socalled bond characterization matrix (BCM) to allow the proper measure on the structural similarity. The BCM technique was then employed to eliminate similar structures and define the desirable local search spaces. We find that the introduction of point group symmetries into generation of cluster structures enables structural diversity and apparently avoids the generation of liquidlike (or disordered) clusters for large systems, thus considerably improving the structural search efficiency. We have incorporated Metropolis criterion into our method to further enhance the structural evolution towards lowenergy regimes of potential energy surfaces. Our method has been extensively benchmarked on LennardJones clusters with different sizes up to 150 atoms and applied into prediction of new structures of mediumsized Lin (n = 20, 40, 58) clusters. High search efficiency was achieved, demonstrating the reliability of the current methodology and its promise as a major method on cluster structure prediction.
Selfregulating and selfevolving particle swarm optimizer
NASA Astrophysics Data System (ADS)
Wang, HuiMin; Qiao, ZhaoWei; Xia, ChangLiang; Li, LiangYu
20150101
In this article, a novel selfregulating and selfevolving particle swarm optimizer (SSPSO) is proposed. Learning from the idea of direction reversal, selfregulating behaviour is a modified position update rule for particles, according to which the algorithm improves the best position to accelerate convergence in situations where the traditional update rule does not work. Borrowing the idea of mutation from evolutionary computation, selfevolving behaviour acts on the current best particle in the swarm to prevent the algorithm from prematurely converging. The performance of SSPSO and four other improved particle swarm optimizers is numerically evaluated by unimodal, multimodal and rotated multimodal benchmark functions. The effectiveness of SSPSO in solving realworld problems is shown by the magnetic optimization of a Halbachbased permanent magnet machine. The results show that SSPSO has good convergence performance and high reliability, and is well matched to actual problems.
Quantum Particles From Quantum Information
NASA Astrophysics Data System (ADS)
Görnitz, T.; Schomäcker, U.
20120801
Many problems in modern physics demonstrate that for a fundamental entity a more general conception than quantum particles or quantum fields are necessary. These concepts cannot explain the phenomena of dark energy or the mindbodyinteraction. Instead of any kind of "small elementary building bricks", the Protyposis, an abstract and absolute quantum information, free of special denotation and open for some purport, gives the solution in the search for a fundamental substance. However, as long as at least relativistic particles are not constructed from the Protyposis, such an idea would remain in the range of natural philosophy. Therefore, the construction of relativistic particles without and with rest mass from quantum information is shown.
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.
Gravity inversion of a fault by Particle swarm optimization (PSO).
Toushmalani, Reza
20130101
Particle swarm optimization is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. In this paper we introduce and use this method in gravity inverse problem. We discuss the solution for the inverse problem of determining the shape of a fault whose gravity anomaly is known. Application of the proposed algorithm to this problem has proven its capability to deal with difficult optimization problems. The technique proved to work efficiently when tested to a number of models.
Particle Swarm Optimization with WattsStrogatz Model
NASA Astrophysics Data System (ADS)
Zhu, Zhuanghua
Particle swarm optimization (PSO) is a popular swarm intelligent methodology by simulating the animal social behaviors. Recent study shows that this type of social behaviors is a complex system, however, for most variants of PSO, all individuals lie in a fixed topology, and conflict this natural phenomenon. Therefore, in this paper, a new variant of PSO combined with WattsStrogatz smallworld topology model, called WSPSO, is proposed. In WSPSO, the topology is changed according to WattsStrogatz rules within the whole evolutionary process. Simulation results show the proposed algorithm is effective and efficient.
Differential evolution for manyparticle adaptive quantum metrology.
Lovett, Neil B; Crosnier, Cécile; PerarnauLlobet, Martí; Sanders, Barry C
20130531
We devise powerful algorithms based on differential evolution for adaptive manyparticle quantum metrology. Our new approach delivers adaptive quantum metrology policies for feedback control that are ordersofmagnitude more efficient and surpass the fewdozenparticle limitation arising in methods based on particleswarm optimization. We apply our method to the binarydecisiontree model for quantumenhanced phase estimation as well as to a new problem: a decision tree for adaptive estimation of the unknown bias of a quantum coin in a quantum walk and show how this latter case can be realized experimentally.
A SynchronousAsynchronous Particle Swarm Optimisation Algorithm
Ab Aziz, Nor Azlina; Mubin, Marizan; Mohamad, Mohd Saberi; Ab Aziz, Kamarulzaman
20140101
In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (SPSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (APSO) has been proposed as an alternative to SPSO. A particle in APSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronousasynchronous PSO (SAPSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm's best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five wellknown unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SAPSO, which is compared with the performances of SPSO and APSO. The results are statistically analysed and show that the proposed SAPSO has performed consistently well. PMID:25121109
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.
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.
Parallel Global Optimization with the Particle Swarm Algorithm (Preprint)
20041201
Waagen, and A. E. Eiben , editors, Evolutionary Programming VII, pages 591–600, Berlin, 1998. Springer. Lecture Notes in Computer Science 1447. 16. R...applications, and resources. In Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, pages 81–86, COEX, World Trade Center, 159 Samseongdong...Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2001), Korea, 2730 May 2001. IEEE Press. 11. J.F. Schutte. Particle swarms in sizing
Earth Observing Satellite Orbit Design Via Particle Swarm Optimization
20140801
Earth Observing Satellite Orbit Design Via Particle Swarm Optimization Sharon Vtipil ∗ and John G. Warner ∗ US Naval Research Laboratory, Washington...DC, 20375, United States Designing the orbit of an Earth observing satellite is generally tedious work. Typically, a large number of numerical...orbit parameters. This methodology only pertains to a single satellite in a circular orbit. I. Introduction Designing the orbit of an Earth observing
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.
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.
Particle swarm optimization for programming deep brain stimulation arrays
NASA Astrophysics Data System (ADS)
Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.
20170201
Objective. Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multiobjective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellarreceiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main results. The algorithm solved the multiobjective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum perelectrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n = 3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multicompartment axon
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
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.
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)
Parameter extraction of solar cells using particle swarm optimization
NASA Astrophysics Data System (ADS)
Ye, Meiying; Wang, Xiaodong; Xu, Yousheng
20090501
In this article, particle swarm optimization (PSO) was applied to extract the solar cell parameters from illuminated currentvoltage characteristics. The performance of the PSO was compared with the genetic algorithms (GAs) for the single and double diode models. Based on synthetic and experimental currentvoltage data, it has been confirmed that the proposed method can obtain higher parameter precision with better computational efficiency than the GA method. Compared with conventional gradientbased methods, even without a good initial guess, the PSO method can obtain the parameters of solar cells as close as possible to the practical parameters only based on a broad range specified for each of the parameters.
PMSM Driver Based on Hybrid Particle Swarm Optimization and CMAC
NASA Astrophysics Data System (ADS)
Tu, Ji; Cao, Shaozhong
A novel hybrid particle swarm optimization (PSO) and cerebellar model articulation controller (CMAC) is introduced to the permanent magnet synchronous motor (PMSM) driver. PSO can simulate the random learning among the individuals of population and CMAC can simulate the selflearning of an individual. To validate the ability and superiority of the novel algorithm, experiments and comparisons have been done in MATLAB/SIMULINK. Analysis among PSO, hybrid PSOCMAC and CMAC feedforward control is also given. The results prove that the electric torque ripple and torque disturbance of the PMSM driver can be reduced by using the hybrid PSOCMAC algorithm.
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.
Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong
20170101
Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimizationbased localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is threefold. First, it surveys the popular particle swarm optimization variants and particle swarm optimizationbased localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the secondorder cone programming algorithm. PMID:28257060
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
Parallel global optimization with the particle swarm algorithm.
Schutte, J F; Reinbolt, J A; Fregly, B J; Haftka, R T; George, A D
20041207
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.
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.
Distributed Adaptive Particle Swarm Optimizer in Dynamic Environment
Cui, Xiaohui; Potok, Thomas E
20070101
In the real world, we have to frequently deal with searching and tracking an optimal solution in a dynamical and noisy environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the changing solution. Particle Swarm Optimization (PSO) is a populationbased stochastic optimization technique, which can find an optimal, or near optimal, solution to a numerical and qualitative problem. In PSO algorithm, the problem solution emerges from the interactions between many simple individual agents called particles, which make PSO an inherently distributed algorithm. However, the traditional PSO algorithm lacks the ability to track the optimal solution in a dynamic and noisy environment. In this paper, we present a distributed adaptive PSO (DAPSO) algorithm that can be used for tracking a nonstationary optimal solution in a dynamically changing and noisy environment.
Improving Vector Evaluated Particle Swarm Optimisation Using Multiple Nondominated Leaders
Lim, Kian Sheng; Buyamin, Salinda; Ahmad, Anita; Shapiai, Mohd Ibrahim; Naim, Faradila; Mubin, Marizan; Kim, Dong Hwa
20140101
The vector evaluated particle swarm optimisation (VEPSO) algorithm was previously improved by incorporating nondominated solutions for solving multiobjective optimisation problems. However, the obtained solutions did not converge close to the Pareto front and also did not distribute evenly over the Pareto front. Therefore, in this study, the concept of multiple nondominated leaders is incorporated to further improve the VEPSO algorithm. Hence, multiple nondominated solutions that are best at a respective objective function are used to guide particles in finding optimal solutions. The improved VEPSO is measured by the number of nondominated solutions found, generational distance, spread, and hypervolume. The results from the conducted experiments show that the proposed VEPSO significantly improved the existing VEPSO algorithms. PMID:24883386
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.
Dimensionality Reduction Particle Swarm Algorithm for High Dimensional Clustering
Cui, Xiaohui; ST Charles, Jesse Lee; Potok, Thomas E; Beaver, Justin M
20080101
The Particle Swarm Optimization (PSO) clustering algorithm can generate more compact clustering results than the traditional Kmeans clustering algorithm. However, when clustering high dimensional datasets, the PSO clustering algorithm is notoriously slow because its computation cost increases exponentially with the size of the dataset dimension. Dimensionality reduction techniques offer solutions that both significantly improve the computation time, and yield reasonably accurate clustering results in high dimensional data analysis. In this paper, we introduce research that combines different dimensionality reduction techniques with the PSO clustering algorithm in order to reduce the complexity of high dimensional datasets and speed up the PSO clustering process. We report significant improvements in total runtime. Moreover, the clustering accuracy of the dimensionality reduction PSO clustering algorithm is comparable to the one that uses full dimension space.
Constraint Web Service Composition Based on Discrete Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Fang, Xianwen; Fan, Xiaoqin; Yin, Zhixiang
Web service composition provides an open, standardsbased approach for connecting web services together to create higherlevel business processes. The Standards are designed to reduce the complexity required to compose web services, hence reducing time and costs, and increase overall efficiency in businesses. This paper present independent global constrains web service composition optimization methods based on Discrete Particle Swarm Optimization (DPSO) and associate Petri net (APN). Combining with the properties of APN, an efficient DPSO algorithm is presented which is used to search a legal firing sequence in the APN model. Using legal firing sequences of the Petri net makes the service composition locating space based on DPSO shrink greatly. Finally, for comparing our methods with the approximating methods, the simulation experiment is given out. Theoretical analysis and experimental results indicate that this method owns both lower computation cost and higher success ratio of service composition.
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
Constructing DNA Barcode Sets based on Particle Swarm Optimization.
Waang, Bin; Zheng, Xuedong; Zhou, Shihua; Zhou, Changjun; Wei, Xiaopeng; Zhang, Qiang; Wei, Ziqi
20170307
Following the completion of the human genome project, a large amount of highthroughput biodata was generated. To analyze these data, massively parallel sequencing, namely nextgeneration sequencing, was rapidly developed. DNA barcodes are used to identify the ownership between sequences and samples when they are attached at the beginning or end of sequencing reads. Constructing DNA barcode sets provides the candidate DNA barcodes for this application. To increase the accuracy of DNA barcode sets, a particle swarm optimization (PSO) algorithm has been modified and used to construct the DNA barcode sets in this paper. Compared with the extant results, some lower bounds of DNA barcode sets are improved. The results show that the proposed algorithm is effective in constructing DNA barcode sets.
Order2 Stability Analysis of Particle Swarm Optimization.
Liu, Qunfeng
20150101
Several stability analyses and stable regions of particle swarm optimization (PSO) have been proposed before. The assumption of stagnation and different definitions of stability are adopted in these analyses. In this paper, the order2 stability of PSO is analyzed based on a weak stagnation assumption. A new definition of stability is proposed and an order2 stable region is obtained. Several existing stable analyses for canonical PSO are compared, especially their definitions of stability and the corresponding stable regions. It is shown that the classical stagnation assumption is too strict and not necessary. Moreover, among all these definitions of stability, it is shown that our definition requires the weakest conditions, and additional conditions bring no benefit. Finally, numerical experiments are reported to show that the obtained stable region is meaningful. A new parameter combination of PSO is also shown to be good, even better than some known best parameter combinations.
Particle swarm optimization based space debris surveillance network scheduling
NASA Astrophysics Data System (ADS)
Jiang, Hai; Liu, Jing; Cheng, HaoWen; Zhang, Yao
20170201
The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of inorbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.
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
Research on particle swarm optimization algorithm based on optimal movement probability
NASA Astrophysics Data System (ADS)
Ma, Jianhong; Zhang, Han; He, Baofeng
20170101
The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.
Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions
NASA Astrophysics Data System (ADS)
Basu, Mousumi
20161201
Particle swarm optimization (PSO) performs well for small dimensional and less complicated problems but fails to locate global minima for complex multiminima functions. This paper proposes an improved particle swarm optimization (IPSO) which introduces Gaussian random variables in velocity term. This improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the speed of convergence and the simplicity of the structure of particle swarm optimization. The algorithm is experimentally validated on 17 benchmark functions and the results demonstrate good performance of the IPSO in solving unimodal and multimodal problems. Its high performance is verified by comparing with two popular PSO variants.
NASA Astrophysics Data System (ADS)
Sarkar, Soham; Das, Swagatam
In recent years particle swarm optimization emerges as one of the most efficient global optimization tools. In this paper, a hybrid particle swarm with differential evolution operator, termed DEPSO, is applied for the synthesis of linear array geometry. Here, the minimum side lobe level and null control, both are obtained by optimizing the spacing between the array elements by this technique. Moreover, a statistical comparison is also provided to establish its performance against the results obtained by Genetic Algorithm (GA), classical Particle Swarm Optimization (PSO), Tabu Search Algorithm (TSA), Differential Evolution (DE) and Memetic Algorithm (MA).
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
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).
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.
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.
Panorama parking assistant system with improved particle swarm optimization method
NASA Astrophysics Data System (ADS)
Cheng, Ruzhong; Zhao, Yong; Li, Zhichao; Jiang, Weigang; Wang, Xin'an; Xu, Yong
20131001
A panorama parking assistant system (PPAS) for the automotive aftermarket together with a practical improved particle swarm optimization method (IPSO) are proposed in this paper. In the PPAS system, four fisheye cameras are installed in the vehicle with different views, and four channels of video frames captured by the cameras are processed as a 360deg topview image around the vehicle. Besides the embedded design of PPAS, the key problem for image distortion correction and mosaicking is the efficiency of parameter optimization in the process of camera calibration. In order to address this problem, an IPSO method is proposed. Compared with other parameter optimization methods, the proposed method allows a certain range of dynamic change for the intrinsic and extrinsic parameters, and can exploit only one reference image to complete all of the optimization; therefore, the efficiency of the whole camera calibration is increased. The PPAS is commercially available, and the IPSO method is a highly practical way to increase the efficiency of the installation and the calibration of PPAS in automobile 4S shops.
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
Discrete Particle Swarm Optimization with Scout Particles for Library Materials Acquisition
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
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.
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.
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.
Evaluation of a particle swarm algorithm for biomechanical optimization.
Schutte, Jaco F; Koh, ByungIl; Reinbolt, Jeffrey A; Haftka, Raphael T; George, Alan D; Fregly, Benjamin J
20050601
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 algorithmsa 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.
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.
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.
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.
NASA Astrophysics Data System (ADS)
Fu, ZhongLiang; Wan, Bin
20091001
The research of the regional ecological environment becomes more important to regional Sustainable Development in order to achieve the harmonious relationship between the person and the nature. The advent of spatial information technologies, such as GIS, GPS and RS, have great enhanced our capabilities to collect and capture spatial data. How to discover potentially useful information and knowledge from massive amounts of spatial data is becoming a crucial project for spatial analysis and spatial decision making. Particle Swarm Optimization has a powerful ability for reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective way to spatial data mining. This paper focuses on construction and learning a Particle Swarm Optimization model for spatial data mining. Firstly, the theory of spatial data mining is introduced and the characteristics of Particle Swarm Optimization are discussed. A framework and process of spatial data mining is proposed. Then we construct a Particle Swarm Optimization model for spatial data mining with the given dataset. The research area is focused on the distribution of pollution sources in Wuhan City. The experimental results demonstrate the feasibility and practical of the proposed approach to spatial data mining. Finally, draw a conclusion and show further avenues for research. Through the empirical study, it has been proved that Particle Swarm Optimization algorithm is feasible and the conclusion can provide instruction for local environmental planning.
Yu, Xiang; Zhang, Xueqing
20170101
Comprehensive learning particle swarm optimization (CLPSO) is a powerful stateoftheart singleobjective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle’s personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run. PMID:28192508
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
The path planning of UAV based on orthogonal particle swarm optimization
NASA Astrophysics Data System (ADS)
Liu, Xin; Wei, Haiguang; Zhou, Chengping; Li, Shujing
20131001
To ensure the attack mission success rate, a trajectory with high survivability and accepted path length and multiple paths with different attack angles must be planned. This paper proposes a novel path planning algorithm based on orthogonal particle swarm optimization, which divides population individual and speed vector into independent orthogonal parts, velocity and individual part update independently, this improvement advances optimization effect of traditional particle swarm optimization in the field of path planning, multiple paths are produced by setting different attacking angles, this method is simulated on electronic chart, the simulation result shows the effect of this method.
Adaptive particle swarm optimization for optimal orbital elements of binary stars
NASA Astrophysics Data System (ADS)
Attia, AbdelFattah
20161201
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.
Solution to Electric Power Dispatch Problem Using Fuzzy Particle Swarm Optimization Algorithm
NASA Astrophysics Data System (ADS)
Chaturvedi, D. K.; Kumar, S.
20150301
This paper presents the application of fuzzy particle swarm optimization to constrained economic load dispatch (ELD) problem of thermal units. Several factors such as quadratic cost functions with valve point loading, ramp rate limits and prohibited operating zone are considered in the computation models. The Fuzzy particle swarm optimization (FPSO) provides a new mechanism to avoid premature convergence problem. The performance of proposed algorithm is evaluated on four test systems. Results obtained by proposed method have been compared with those obtained by PSO method and literature results. The experimental results show that proposed FPSO method is capable of obtaining minimum fuel costs in fewer numbers of iterations.
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.
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.
Wang, Xue; Wang, Sheng; Ma, JunJie
20070101
The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed coevolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the coevolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.
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.
Lithography using quantum entangled particles
NASA Technical Reports Server (NTRS)
Williams, Colin (Inventor); Dowling, Jonathan (Inventor); della Rossa, Giovanni (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.
NASA Astrophysics Data System (ADS)
Ghosh, Pradipta; Zafar, Hamim
Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This paper describes the synthesis method of linear array geometry with minimum side lobe level and null control by the Dynamic MultiSwarm Particle Swarm Optimizer with Local Search (DMSPSO) which optimizes the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. The results of the DMSPSO algorithm have been shown to meet or beat the results obtained using other stateoftheart metaheuristics like the Genetic Algorithm (GA),General Particle Swarm Optimization (PSO), Memetic Algorithms (MA), and Tabu Search (TS) in a statistically meaningful way. Three design examples are presented that illustrate the use of the DMSPSO algorithm, and the optimization goal in each example is easily achieved.
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.
Optimal Pid Tuning for Power System Stabilizers Using Adaptive Particle Swarm Optimization Technique
NASA Astrophysics Data System (ADS)
Oonsivilai, Anant; Marungsri, Boonruang
20081001
An application of the intelligent search technique to find optimal parameters of power system stabilizer (PSS) considering proportionalintegralderivative controller (PID) for a singlemachine infinitebus system is presented. Also, an efficient intelligent search technique, adaptive particle swarm optimization (APSO), is engaged to express usefulness of the intelligent search techniques in tuning of the PID—PSS parameters. Improve damping frequency of system is optimized by minimizing an objective function with adaptive particle swarm optimization. At the same operating point, the PID—PSS parameters are also tuned by the ZieglerNichols method. The performance of proposed controller compared to the conventional ZieglerNichols PID tuning controller. The results reveal superior effectiveness of the proposed APSO based PID controller.
NASA Astrophysics Data System (ADS)
Somasundaram, P.; Muthuselvan, N. B.
This paper presents new computationally efficient improved Particle Swarm algorithms for solving Security Constrained Optimal Power Flow (SCOPF) in power systems with the inclusion of FACTS devices. The proposed algorithms are developed based on the combined application of Gaussian and Cauchy Probability distribution functions incorporated in Particle Swarm Optimization (PSO). The power flow algorithm with the presence of Static Var Compensator (SVC) Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC), has been formulated and solved. The proposed algorithms are tested on standard IEEE 30bus system. The analysis using PSO and modified PSO reveals that the proposed algorithms are relatively simple, efficient, reliable and suitable for realtime applications. And these algorithms can provide accurate solution with fast convergence and have the potential to be applied to other power engineering problems.
NASA Astrophysics Data System (ADS)
Xiong, Yuhong; Liu, Yunxiang; Shu, Minglei
20161001
In the process of actual measurement and analysis of micro near infrared spectrometer, genetic algorithm is used to select the wavelengths and then partial least square method is used for modeling and analyzing. Because genetic algorithm has the disadvantages of slow convergence and difficult parameter setting, and partial least square method in dealing with nonlinear data is far from being satisfactory, the practical application effect of partial least square method based on genetic algorithm is severely affected negatively. The paper introduces the fundamental principles of particle swarm optimization and support vector machine, and proposes a support vector machine method based on particle swarm optimization. The method can overcome the disadvantage of partial least squares method based on genetic algorithm to a certain extent. Finally, the method is tested by an example, and the results show that the method is effective.
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.
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
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.
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.
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. PMID:22163872
Particle swarms in gases: the velocityaverage evolution equations from Newton's law.
Ferrari, Leonardo
20030801
The evolution equation for a generic average quantity relevant to a swarm of particles homogeneously dispersed in a uniform gas, is obtained directly from the Newton's law, without having recourse to the (intermediary) Boltzmann equation. The procedure makes use of appropriate averages of the term resulting from the impulsive force (due to collisions) in the Newton's law. When the background gas is assumed to be in thermal equilibrium, the obtained evolution equation is shown to agree with the corresponding one following from the Boltzmann equation. But the new procedure also allows to treat physical situations in which the Boltzmann equation is not valid, as it happens when some correlation exists (or is assumed) between the velocities of swarm and gas particles.
NASA Astrophysics Data System (ADS)
Hou, Rui; Yu, Junle
20111201
Optical burst switching (OBS) has been regarded as the next generation optical switching technology. In this paper, the routing problem based on particle swarm optimization (PSO) algorithm in OBS has been studies and analyzed. Simulation results indicate that, the PSO based routing algorithm will optimal than the conversional shortest path first algorithm in space cost and calculation cost. Conclusions have certain theoretical significances for the improvement of OBS routing protocols.
Wang, Xue; Ma, JunJie; Wang, Sheng; Bi, DaoWei
20070101
The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energyefficient coverage with distributed particle swarm optimization and simulated annealing. First, the energyefficient coverage problem is formulated with sensing coverage and energy consumption models. We consider the network composed of stationary and mobile nodes. Second, coverage and energy metrics are presented to evaluate the coverage rate and energy consumption of a wireless sensor network, where a grid exclusion algorithm extracts the coverage state and Dijkstra's algorithm calculates the lowest cost path for communication. Then, a hybrid algorithm optimizes the energy consumption, in which particle swarm optimization and simulated annealing are combined to find the optimal deployment solution in a distributed manner. Simulated annealing is performed on multiple wireless sensor nodes, results of which are employed to correct the local and global best solution of particle swarm optimization. Simulations of wireless sensor node deployment verify that coverage performance can be guaranteed, energy consumption of communication is conserved after deployment optimization and the optimization performance is boosted by the distributed algorithm. Moreover, it is demonstrated that energy efficiency of wireless sensor networks is enhanced by the proposed optimization algorithm in target tracking applications.
Adaptive feature selection using vshaped binary particle swarm optimization
Dong, Hongbin; Zhou, Xiurong
20170101
Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on Vshaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using Vshaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the Vshaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers. PMID:28358850
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.
NASA Astrophysics Data System (ADS)
Tapoglou, E.; Trichakis, I. C.; Dokou, Z.; Karatzas, G. P.
20120401
The purpose of this study is to examine the use of particle swarm optimization algorithm in order to train a feedforward multilayer artificial neural network, which can simulate hydraulic head change at an observation well. Particle swarm optimization is a relatively new evolutionary algorithm, developed by Eberhart and Kennedy (1995), which is used to find optimal solutions to numerical and quantitative problems. Three different variations of particle swarm optimization algorithm are considered, the classic algorithm with the improvement of inertia weight, PSOTVAC and GLBestPSO. The best performance among all the algorithms was achieved by GLBestPSO, where the distance between the overall best solution found and the best solution of each particle plays a major role in updating each particle's velocity. The algorithm is implemented using field data from the region of Agia, Chania, Greece. The particle swarm optimization algorithm shows an improvement of 9.3% and 18% in training and test errors respectively, compared to the errors of the back propagation algorithm. The trained neural network can predict the hydraulic head change at a well, without being able to predict extreme and transitional phenomena. The maximum divergence from the observed values is 0.35m. When the hydraulic head change is converted into hydraulic head, using the observed hydraulic head of the previous day, the deviations of simulated values from the actual hydraulic head appear comparatively smaller, with an average deviation of 0.041m. The trained neural network was also used for midterm prediction. In this case, the hydraulic head of the first day of the simulation is used together with the hydraulic head change derived from the simulation. The values obtained by this process were smaller than the observed, while the maximum difference is approximately 1m. However, this error, is not accumulated during the two hydrological years of simulation, and the error at the end of the simulation
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)
Ma, Yanfang; Xu, Jiuping
20150601
This article puts forward a cloud theorybased particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bilevel programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theorybased particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency.
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.
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.
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.
NASA Astrophysics Data System (ADS)
Huang, RongHwa; Yang, ChangLin; Hsu, ChunTing
20151201
Flow shop production system  compared to other economically important production systems  is popular in real manufacturing environments. This study focuses on the flow shop with multiprocessor scheduling problem (FSMP), and develops an improved particle swarm optimisation heuristic to solve it. Additionally, this study designs an integer programming model to perform effectiveness and robustness testing on the proposed heuristic. Experimental results demonstrate a 10% to 50% improvement in the effectiveness of the proposed heuristic in smallscale problem tests, and a 10% to 40% improvement in the robustness of the heuristic in largescale problem tests, indicating extremely satisfactory performance.
Evolutionary artificial neural networks by multidimensional particle swarm optimization.
Kiranyaz, Serkan; Ince, Turker; Yildirim, Alper; Gabbouj, Moncef
20091201
In this paper, we propose a novel technique for the automatic design of Artificial Neural Networks (ANNs) by evolving to the optimal network configuration(s) within an architecture space. It is entirely based on a multidimensional Particle Swarm Optimization (MD PSO) technique, which reforms the native structure of swarm particles in such a way that they can make interdimensional passes with a dedicated dimensional PSO process. Therefore, in a multidimensional search space where the optimum dimension is unknown, swarm particles can seek both positional and dimensional optima. This eventually removes the necessity of setting a fixed dimension a priori, which is a common drawback for the family of swarm optimizers. With the proper encoding of the network configurations and parameters into particles, MD PSO can then seek the positional optimum in the error space and the dimensional optimum in the architecture space. The optimum dimension converged at the end of a MD PSO process corresponds to a unique ANN configuration where the network parameters (connections, weights and biases) can then be resolved from the positional optimum reached on that dimension. In addition to this, the proposed technique generates a ranked list of network configurations, from the best to the worst. This is indeed a crucial piece of information, indicating what potential configurations can be alternatives to the best one, and which configurations should not be used at all for a particular problem. In this study, the architecture space is defined over feedforward, fullyconnected ANNs so as to use the conventional techniques such as backpropagation and some other evolutionary methods in this field. The proposed technique is applied over the most challenging synthetic problems to test its optimality on evolving networks and over the benchmark problems to test its generalization capability as well as to make comparative evaluations with the several competing techniques. The experimental
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.
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
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.
NASA Astrophysics Data System (ADS)
Wang, Deguang; Han, Baochang; Huang, Ming
Computer forensics is the technology of applying computer technology to access, investigate and analysis the evidence of computer crime. It mainly include the process of determine and obtain digital evidence, analyze and take data, file and submit result. And the data analysis is the key link of computer forensics. As the complexity of real data and the characteristics of fuzzy, evidence analysis has been difficult to obtain the desired results. This paper applies fuzzy cmeans clustering algorithm based on particle swarm optimization (FCMP) in computer forensics, and it can be more satisfactory results.
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.
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
Wei, HuaLiang; Billings, Stephen A; Zhao, Yifan; Guo, Lingzhong
20090101
In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatiotemporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel twostage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatiotemporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.
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.
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design.
Juang, ChiaFeng
20040401
An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upperhalf of the bestperforming individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a TakagiSugenoKangtype recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority.
NASA Astrophysics Data System (ADS)
Guo, Weian; Li, Wuzhao; Zhang, Qun; Wang, Lei; Wu, Qidi; Ren, Hongliang
20141101
In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeographybased optimization (BBO) to propose a hybrid algorithm termed biogeographybased particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.
Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block.
Kora, Padmavathi; Kalva, Sri Ramakrishna
20150101
Abnormal cardiac beat identification is a key process in the detection of heart diseases. Our present study describes a procedure for the detection of left and right bundle branch block (LBBB and RBBB) Electrocardiogram (ECG) patterns. The electrical impulses that control the cardiac beat face difficulty in moving inside the heart. This problem is termed as bundle branch block (BBB). BBB makes it harder for the heart to pump blood effectively through the heart circulatory system. ECG feature extraction is a key process in detecting heart ailments. Our present study comes up with a hybrid method combining two heuristic optimization methods: Bacterial Forging Optimization (BFO) and Particle Swarm Optimization (PSO) for the feature selection of ECG signals. One of the major controlling forces of BFO algorithm is the chemotactic movement of a bacterium that models a test solution. The chemotaxis process of the BFO depends on random search directions which may lead to a delay in achieving the global optimum solution. The hybrid technique: Bacterial ForgingParticle Swarm Optimization (BFPSO) incorporates the concepts from BFO and PSO and it creates individuals in a new generation. This BFPSO method performs local search through the chemotactic movement of BFO and the global search over the entire search domain is accomplished by a PSO operator. The BFPSO feature values are given as the input for the LevenbergMarquardt Neural Network classifier.
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.
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.
Optimierung von FSSBandpassfiltern mit Hilfe der Schwarmintelligenz (Particle Swarm Optimization)
NASA Astrophysics Data System (ADS)
Wu, G.; Hansen, V.; Kreysa, E.; Gemünd, H.P.
20060901
In diesem Beitrag wird ein neues Verfahren zur Optimierung von Bandpassfiltern aus mehrlagigen frequenzselektiven Schirmen (FSS), die in ein Dielektrikum eingebettet sind, vorgestellt. Das Ziel ist es, die Parameter der gesamten Struktur so zu optimieren, dass ihre Transmissionseigenschaften hohe Filteranforderungen erfüllen. Als Optimierungsverfahren wird die Particle Swarm Optimization (PSO) eingesetzt. PSO ist eine neue stochastische Optimierungsmethode, die in verschieden Gebieten, besonders aber bei der Optimierung nicht linearer Probleme mit mehreren Zielfunktionen erfolgreich eingesetzt wird. In dieser Arbeit wird die PSO in die Spektralbereichsanalyse zur Berechnung komplexer FSSStrukturen integriert. Die numerische Berechnung basiert auf einer Integralgleichungsformulierung mit Hilfe der spektralen Greenschen Funktion für geschichtete Strukturen. This paper presents a novel procedure for the optimization of bandpass filters consisting of frequency selective surfaces (FSS) embedded in a dielectric. The aim is to optimize the parameters of the complete structure so that the transmission characteristics of the filters fulfill the demanding requirements. The Particle Swarm Optimization (PSO) is used as the optimization procedure. PSO is a new stochastic optimization method that is successfully applied in different areas for the optimization of nonlinear problems with several objectfunctions. In this work, PSO is integrated into the spectral domain analysis for the calculation of the complex FSS structures. The numerical computation is based on the formulation of an integral equation with the help of the spectral Green's function for layered media.
Adam, Asrul; Mohd Tumari, Mohd Zaidi; Mohamad, Mohd Saberi
20140101
Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, timefrequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RAPSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RAPSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236
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
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.
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
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
A new logistic dynamic particle swarm optimization algorithm based on random topology.
Ni, Qingjian; Deng, Jianming
20130101
Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.
Xu, ShengHua; Liu, JiPing; Zhang, FuHao; Wang, Liang; Sun, LiJian
20150827
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.
Quantum fingerprinting with a single particle
Massar, S.
20050101
We show that the twoslit experiment in which a single quantum particle interferes with itself can be interpreted as a quantum fingerprinting protocol: the interference pattern exhibited by the particle contains information about the environment it encountered in the slits which would require much more communication to learn classically than is required quantum mechanically. An extension to the case where the particle has many internal degrees of freedom is suggested, and its interpretation is discussed. The interpretation of these results is discussed in detail, and a possible experimental realization is proposed.
Zhang, Bing; Sun, Xu; Gao, LianRu; Yang, LiNa
20110901
For the inaccuracy of endmember extraction caused by abnormal noises of data during the mixed pixel decomposition process, particle swarm optimization (PSO), a swarm intelligence algorithm was introduced and improved in the present paper. By redefining the position and velocity representation and data updating strategies, the algorithm of discrete particle swarm optimization (DPSO) was proposed, which made it possible to search resolutions in discrete space and ultimately resolve combinatorial optimization problems. In addition, by defining objective functions and feasible solution spaces, endmember extraction was converted to combinatorial optimization problem, which can be resolved by DPSO. After giving the detailed flow of applying DPSO to endmember extraction and experiments based on simulative data and real data, it has been verified the algorithm's flexibility to handle data with abnormal noise and the reliability of endmember extraction were verified. Furthermore, the influence of different parameters on the algorithm's performances was analyzed thoroughly.
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
Li, Yongjie; Yao, Dezhong; Yao, Jonathan; Chen, Wufan
20050807
Automatic beam angle selection is an important but challenging problem for intensitymodulated radiation therapy (IMRT) planning. Though many efforts have been made, it is still not very satisfactory in clinical IMRT practice because of overextensive computation of the inverse problem. In this paper, a new technique named BASPSO (Beam Angle Selection with a Particle Swarm Optimization algorithm) is presented to improve the efficiency of the beam angle optimization problem. Originally developed as a tool for simulating social behaviour, the particle swarm optimization (PSO) algorithm is a relatively new populationbased evolutionary optimization technique first introduced by Kennedy and Eberhart in 1995. In the proposed BASPSO, the beam angles are optimized using PSO by treating each beam configuration as a particle (individual), and the beam intensity maps for each beam configuration are optimized using the conjugate gradient (CG) algorithm. These two optimization processes are implemented iteratively. The performance of each individual is evaluated by a fitness value calculated with a physical objective function. A population of these individuals is evolved by cooperation and competition among the individuals themselves through generations. The optimization results of a simulated case with known optimal beam angles and two clinical cases (a prostate case and a headandneck case) show that PSO is valid and efficient and can speed up the beam angle optimization process. Furthermore, the performance comparisons based on the preliminary results indicate that, as a whole, the PSObased algorithm seems to outperform, or at least compete with, the GAbased algorithm in computation time and robustness. In conclusion, the reported work suggested that the introduced PSO algorithm could act as a new promising solution to the beam angle optimization problem and potentially other optimization problems in IMRT, though further studies need to be investigated.
Tang, Mei; Hu, CuiE; Lv, ZhenLong; Chen, XiangRong; Cai, LingCang
20161201
The structures of cationic water clusters (H2O)8(+) have been globally explored by the particle swarm optimization method in combination with quantum chemical calculations. Geometry optimization and vibrational analysis for the 15 most interesting clusters were computed at the MP2/augccpVDZ level and infrared spectrum calculation at MPW1K/6311++G** level. Special attention was paid to the relationships between their configurations and energies. Both MP2 and B3LYPD3 calculations revealed that the cagelike structure is the most stable, which is different from a fivemembered ring lowest energy structure but agrees well with a cagelike structure in the literature. Furthermore, our obtained cagelike structure is more stable by 0.87 and 1.23 kcal/mol than the previously reported structures at MP2 and B3LYPD3 levels, respectively. Interestingly, on the basis of their relative Gibbs free energies and the temperature dependence of populations, the cagelike structure predominates only at very low temperatures, and the most dominating species transforms into a newfound fourmembered ring structure from 100 to 400 K, which can contribute greatly to the experimental infrared spectrum. By topological analysis and reduced density gradient analysis, we also investigated the structural characteristics and bonding strengths of these water cluster radical cations.
Quantum particle interacting with a metallic particle: Spectra from quantum Langevin theory
NASA Astrophysics Data System (ADS)
Loh, W. M. Edmund; Ooi, C. H. Raymond
20170101
The effect of a nearby metallic particle on the quantum optical properties of a quantum particle in the fourlevel double Raman configuration is studied using the quantum Langevin approach. We obtain analytical expressions for the correlated quantum fields of Stokes and antiStokes photons emitted from the system and perform analysis on how the interparticle distance, the direction of observation or detection, the strengths of controllable laser fields, the presence of surface plasmon resonance, and the number density of the quantum particle affect the quantum spectra of the Stokes and antiStokes fields. We explore the physics behind the quantumparticlemetallicnanoparticle interaction within the dipole approximation, that is, when the interparticle distance is much larger than the sizes of the particles. Our results show the dependence of the spectra on the interparticle distance in the form of oscillatory behavior with damping as the interparticle distance increases. At weaker laser fields the enhancement of quantum fields which manifests itself in the form of a Fano dip in the central peak of the spectra becomes significant. Also, the quantumparticlemetallicnanoparticle coupling, which is affected by the size of the metallic nanoparticle and the number density of the quantum particle, changes the angular dependence of the spectra by breaking the angular rotational symmetry. In the presence of surface plasmon resonance the oscillatory dependence of the spectra on the interparticle distance and angles of observation becomes even stronger due to the plasmonic enhancement effect.
Yang, Jie; Zhang, Pengcheng; Zhang, Liyuan; Shu, Huazhong; Li, Baosheng; Gui, Zhiguo
20170101
In inverse treatment planning of intensitymodulated radiation therapy (IMRT), the objective function is typically the sum of the weighted subscores, where the weights indicate the importance of the subscores. To obtain a highquality treatment plan, the planner manually adjusts the objective weights using a trialanderror procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle's location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle's location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy  the crossover and mutation operator hybrid approach  is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded highquality plans for all of the cases, without human
A Particle Swarm OptimizationBased Approach with Local Search for Predicting Protein Folding.
Yang, ChengHong; Lin, YuShiun; Chuang, LiYeh; Chang, HsuehWei
20170313
The hydrophobicpolar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hillclimbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HELPSO algorithm. By using 20 known protein structures, we evaluated the performance of the HELPSO algorithm in predicting protein folding in the HP model. The proposed HELPSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HELPSO algorithm yielded optimal solutions for all predicted protein folding structures. All HELPSOpredicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.
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.
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.
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.
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.
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
Yang, Chenying; Hong, Liang; Shen, Weidong; Zhang, Yueguang; Liu, Xu; Zhen, Hongyu
20130422
We propose three color filters (red, green, blue) based on a twodimensional (2D) grating, which maintain the same perceived specular colors for a broad range of incident angles with the average polarization. Particle swarm optimization (PSO) method is employed to design these filters for the first time to our knowledge. Two merit functions involving the reflectance curves and color difference in CIEDE2000 formula are respectively constructed to adjust the structural parameters during the optimization procedure. Three primary color filters located at 637nm, 530nm and 446nm with high saturation are obtained with the peak reflectance of 89%, 83%, 66%. The reflectance curves at different incident angles are coincident and the color difference is less than 8 for the incident angle up to 45°. The electric field distribution of the structure is finally studied to analyze the optical property.
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.
AlAsadi, H A; AlMansoori, M H; Hitam, S; Saripan, M I; Mahdi, M A
20110131
We implement a particle swarm optimization (PSO) algorithm to characterize stimulated Brillouin scattering phenomena in optical fibers. The explicit and strong dependence of the threshold exponential gain on the numerical aperture, the pump laser wavelength and the optical loss coefficient are presented. The proposed PSO model is also evaluated with the localized, nonfluctuating source model and the distributed (nonlocalized) fluctuating source model. Using our model, for fiber lengths from 1 km to 29 km, the calculated threshold exponential gain of stimulated Brillouin scattering is gradually decreased from 17.4 to 14.6 respectively. The theoretical results of Brillouin threshold power predicted by the proposed PSO model show a good agreement with the experimental results for different fiber lengths from 1 km to 12 km.
Zhang, Qi; Wang, Yuanyuan; Ma, Jianying; Shi, Jun
20110101
It is valuable for diagnosis of atherosclerosis to detect lumen and mediaadventitia contours in intravascular ultrasound (IVUS) images of atherosclerotic plaques. In this paper, a method for contour detection of plaques is proposed utilizing the prior knowledge of elliptic geometry of plaques. Contours are initialized as ellipses by using ellipse template matching, where a matching function is maximized by particle swarm optimization. Then the contours are refined by boundary vector field snakes. The method was evaluated via 88 in vivo images from 21 patients. It outperformed a stateoftheart method by 3.8 pixels and 4.8% in terms of the mean distance error and relative mean distance error, respectively.
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
Chen, ShyiMing; Hsin, WenChyuan
20150701
In this paper, we propose a new weighted fuzzy interpolative reasoning method for sparse fuzzy rulebased systems based on the slopes of fuzzy sets. We also propose a particle swarm optimization (PSO)based weightslearning algorithm to automatically learn the optimal weights of the antecedent variables of fuzzy rules for weighted fuzzy interpolative reasoning. We apply the proposed weighted fuzzy interpolative reasoning method using the proposed PSObased weightslearning algorithm to deal with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems. The experimental results show that the proposed weighted fuzzy interpolative reasoning method using the proposed PSObased weightslearning algorithm outperforms the existing methods for dealing with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems.
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.
Iris recognition using Gabor filters optimized by the particle swarm algorithm
NASA Astrophysics Data System (ADS)
Tsai, ChungChih; Taur, JinShiuh; Tao, ChinWang
20090401
An efficient feature extraction algorithm based on optimized Gabor filters and a relative variation analysis approach is proposed for iris recognition. The Gabor filters are optimized by using the particle swarm algorithm to adjust the parameters. Moreover, a sequential scheme is developed to determine the number of filters in the optimal Gabor filter bank. In the preprocessing step, the lower part of the iris image is unwrapped and normalized to a rectangular block that is then decomposed by the optimal Gabor filters. After that, a simple encoding method is adopted to generate a compact iris code. Experimental results show that with a smaller iris code size, the proposed method can produce comparable performance to that of the existing iris recognition systems.
NASA Astrophysics Data System (ADS)
Wu, Qi
20100301
Demand forecasts play a crucial role in supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Aiming at demand series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the space (quadratic continuous integral space). In this paper, we present a hybrid intelligent system combining the wavelet kernel support vector machine and particle swarm optimization for demand forecasting. The results of application in car sale series forecasting show that the forecasting approach based on the hybrid PSOWvSVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves that this method is, for the discussed example, better than hybrid PSOvSVM and other traditional methods.
NASA Astrophysics Data System (ADS)
Jude Hemanth, Duraisamy; Umamaheswari, Subramaniyan; Popescu, Daniela Elena; Naaji, Antoanela
20160101
Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT) and Finite Ridgelet Transform (FRIT) are used in combination with GA and PSO to improve the efficiency of the image steganography system.
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.
Hierarchical winnertakeall particle swarm optimization social network for neural model fitting.
Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L
20170201
Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winnertakeall coding found in visual cortical neurons. We show that the winnertakeall network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.
NASA Astrophysics Data System (ADS)
Yang, Yue; Wen, Jian; Chen, Xiaofei
20150701
In this paper, we apply particle swarm optimization (PSO), an artificial intelligence technique, to velocity calibration in microseismic monitoring. We ran simulations with four 1D layered velocity models and three different initial model ranges. The results using the basic PSO algorithm were reliable and accurate for simple models, but unsuccessful for complex models. We propose the staged shrinkage strategy (SSS) for the PSO algorithm. The SSSPSO algorithm produced robust inversion results and had a fast convergence rate. We investigated the effects of PSO's velocity clamping factor in terms of the algorithm reliability and computational efficiency. The velocity clamping factor had little impact on the reliability and efficiency of basic PSO, whereas it had a large effect on the efficiency of SSSPSO. Reassuringly, SSSPSO exhibits marginal reliability fluctuations, which suggests that it can be confidently implemented.
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.
Design Optimization of Pin Fin Geometry Using Particle Swarm Optimization Algorithm
Hamadneh, Nawaf; Khan, Waqar A.; Sathasivam, Saratha; Ong, Hong Choon
20130101
Particle swarm optimization (PSO) is employed to investigate the overall performance of a pin fin.The following study will examine the effect of governing parameters on overall thermal/fluid performance associated with different fin geometries, including, rectangular plate fins as well as square, circular, and elliptical pin fins. The idea of entropy generation minimization, EGM is employed to combine the effects of thermal resistance and pressure drop within the heat sink. A general dimensionless expression for the entropy generation rate is obtained by considering a control volume around the pin fin including base plate and applying the conservations equations for mass and energy with the entropy balance. Selected fin geometries are examined for the heat transfer, fluid friction, and the minimum entropy generation rate corresponding to different parameters including axis ratio, aspect ratio, and Reynolds number. The results clearly indicate that the preferred fin profile is very dependent on these parameters. PMID:23741525
A Novel Method for Edge Detection in Images Based on Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Baby Sherin, C.; Mredhula, L.
20170101
Edges give important structural information about the images. Edge detection is a process of identifying and locating the edges in an image. Edges are the points where discontinuity of intensity occurs. It also represents the boundaries of objects in images. In this paper a new edge detection method based on Particle Swarm Optimization is discussed. The proposed method uses morphological operations and a thresholding technique to improve the result of edge detector. This algorithm performs better in images comparing to other traditional methods of edge detection. The performance of proposed method is compared with traditional edge detection methods such as Sobel, Prewitt, Laplacian of Gaussian and Canny with parameters Baddeley's Delta Metric. Statistical analysis is performed to evaluate accuracy of edge detection techniques.
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.
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.
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.
NASA Astrophysics Data System (ADS)
Kela, K. B.; Arya, L. D.
20140901
This paper describes a methodology for determination of optimum failure rate and repair time for each section of a radial distribution system. An objective function in terms of reliability indices and their target values is selected. These indices depend mainly on failure rate and repair time of a section present in a distribution network. A cost is associated with the modification of failure rate and repair time. Hence the objective function is optimized subject to failure rate and repair time of each section of the distribution network considering the total budget allocated to achieve the task. The problem has been solved using differential evolution and bare bones particle swarm optimization. The algorithm has been implemented on a sample radial distribution system.
NASA Astrophysics Data System (ADS)
Miyazaki, Takahiko; Akisawa, Atsushi; Kashiwagi, Takao
The cogeneration system provides electricity as well as heating and cooling, which consequently leads to a complexity of the design and operation of the system. It requires, therefore, the optimization of parameters such as the number of machines and the capacity of equipment. Generally, the problem can be expressed as a mixed integer nonlinear programming problem, and a lot of efforts would be required to solve it. In this paper, we present a different approach to the optimization of cogeneration systems, which facilitates to find a quasioptimum solution. The particle swarm optimization combined with a simulation of the system is applied to the minimization of the primary energy consumption and of the system cost. The results present the optimum system constitutions for medium and largesized buildings. The result of the system cost minimization under a constraint of the energy saving rate is also discussed.
A particle swarm optimization variant with an inner variable learning strategy.
Wu, Guohua; Pedrycz, Witold; Ma, Manhao; Qiu, Dishan; Li, Haifeng; 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.
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.
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.
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.
Optimization of the Infrastructure of Reinforced Concrete Reservoirs by a Particle Swarm Algorithm
NASA Astrophysics Data System (ADS)
Kia, Saeed; Sebt, Mohammad Hassan; Shahhosseini, Vahid
20150301
Optimization techniques may be effective in finding the best modeling and shapes for reinforced concrete reservoirs (RCR) to improve their durability and mechanical behavior, particularly for avoiding or reducing the bending moments in these structures. RCRs are one of the major structures applied for reserving fluids to be used in drinking water networks. Usually, these structures have fixed shapes which are designed and calculated based on input discharges, the conditions of the structure's topology, and geotechnical locations with various combinations of static and dynamic loads. In this research, the elements of reservoir walls are first typed according to the performance analyzed; then the range of the membrane based on the thickness and the minimum and maximum cross sections of the bar used are determined in each element. This is done by considering the variable constraints, which are estimated by the maximum stress capacity. In the next phase, based on the reservoir analysis and using the algorithm of the PARIS connector, the related information is combined with the code for the PSO algorithm, i.e., an algorithm for a swarming search, to determine the optimum thickness of the cross sections for the reservoir membrane's elements and the optimum cross section of the bar used. Based on very complex mathematical linear models for the correct embedding and angles related to achain of peripheral strengthening membranes, which optimize the vibration of the structure, a mutual relation is selected between the modeling software and the code for a particle swarm optimization algorithm. Finally, the comparative weight of the concrete reservoir optimized by the peripheral strengthening membrane is analyzed using common methods. This analysis shows a 19% decrease in the bar's weight, a 20% decrease in the concrete's weight, and a minimum 13% saving in construction costs according to the items of a checklist for a concrete reservoir at 10,000 m3.
Highresolution microwave diagnostics of architectural components by particle swarm optimization
NASA Astrophysics Data System (ADS)
Genovesi, Simone; Salerno, Emanuele; Monorchio, Agostino; Manara, Giuliano
20100501
the discretization grid used by the forward solver. The algorithm we chose to optimize the objective is based on the particle swarm paradigm. Each feasible solution is coded as a location in a multidimensional space, explored by a number of "particles" each moving with a certain velocity, which is partly random and partly induced by the experience of both the particle itself and the "swarm" of all the other particles. In our case, the search is complicated by the mixed continuousbinary nature of our unknowns, but the swarm intelligence approach maintains the advantage of its intrinsic parallelism. The experimental results we obtained from both simulated and real measurements show that, for typical permittivity values and radiation wavelengths, the spatial resolution is highly improved by the line process. From real measurements in the range 1.72.6 GHz, we accurately reconstructed the permittivity values of our test phantom and located the discontinuities within the limits imposed by our discretization grid (with 1.5 mm cell thickness). At present, the applicability of our reconstruction method is still limited by the forward solver, which is based on a cascaded transmissionline model that assumes normal and planewave incidence. We are developing a new solver based on a closedform Green's function in multilayered media, which should enable us to model appropriately both the microwave sensor and the illumination geometry, thus improving the accuracy of the computed reflection coefficients in the objective functional.
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.
Detecting the relative localisation of quantum particles
NASA Astrophysics Data System (ADS)
Knott, P. A.; Sindt, J.; Dunningham, J. A.
20130601
One interpretation of how the classical world emerges from quantum physics involves the buildup of certain robust entangled states between particles due to scattering events [1]. This is intriguing because it links classical behaviour with the uniquely quantum effect of entanglement and differs from other interpretations that say classicality arises when quantum correlations are lost or neglected in measurements. However, the problem with this new interpretation has been finding an experimental way of verifying it. Here we outline a straightforward scheme that enables just that and should, in principle, allow experiments to confirm the theory to any desired degree of accuracy.
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.
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.
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.
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.
CCDBased Imaging of LowEnergy Charged Particle Distribution Functions on ePOP and Swarm
NASA Astrophysics Data System (ADS)
Knudsen, D. J.; Burchill, J. K.
20131201
The Canadian Enhanced Polar Outflow Probe (ePOP) and the European Space Agency's three Swarm satellites are being readied for launch in September and November 2013, respectively. Each will carry instruments that incorporate a novel CCDbased chargedparticle detector to provide 64pixeldiameter images of 2D, lowenergy charged particle distributions. The ePOP Suprathermal Electron Imager (SEI) will produce distribution functions in pitch angle and energy up to 200 eV at rates of up to 100 per second, with the goal of characterizing photo and suprathermal electrons that can drive ion outflow. The SEI can also image ion distributions up to 20 eV. ePOP will be launched on a SpaceX Falcon 9 rocket into a polar elliptical orbit with an apogee of 1500 km. The Swarm satellites will be launched on a Russian Rokot vehicle into circular polar orbits, two at an initial altitude of 450 km, the third at 530 km. Swarm will measure magnetic and electric fields, the latter indirectly through ion drift detected by two Thermal Ion Imagers (TII) in each instrument, with the aid of Langmuir probe measurements of spacecraft potential and electron density and temperature. Electric fields measurements will be produced at a cadence of 2 per second to produce a picture of ionospheric electrodynamics at scales from 4 km to global. Due to special emphasis on measurement precision, Swarm will be able to resolve variations in Poynting flux as small as 1 microWatt per square meter. We gratefully acknowledge the ePOP SEI technical development team at the University of Calgary, and funding from the Canadian Space Agency. The Swarm Electric Field Instruments were built by a COM DEV Canada in collaboration with the University of Calgary and the Swedish Institute for Space Physics in Uppsala, with funding from ESA and CSA.
NASA Astrophysics Data System (ADS)
Dujko, Sasa
20160901
In this work we review the progress achieved over the last few decades in the fundamental kinetic theory of charged particle swarms with the focus on numerical techniques for the solution of Boltzmann's equation for electrons, as well as on the development of fluid models. We present a timedependent multi term solution of Boltzmann's equation valid for electrons and positrons in varying configurations of electric and magnetic fields. The capacity of a theory and associated computer code will be illustrated by considering the heating mechanisms for electrons in radiofrequency electric and magnetic fields in a collisiondominated regime under conditions when electron transport is greatly affected by nonconservative collisions. The kinetic theory for solving the Boltzmann equation will be followed by a fluid equation description of charged particle swarms in both the hydrodynamic and nonhydrodynamic regimes, highlighting (i) the utility of momentum transfer theory for evaluating collisional terms in the balance equations and (ii) closure assumptions and approximations. The applications of this theory are split into three sections. First, we will present our 1.5D model of Resistive Plate Chambers (RPCs) which are used for timing and triggering purposes in many high energy physics experiments. The model is employed to study the avalanche to streamer transition in RPCs under the influence of space charge effects and photoionization. Second, we will discuss our highorder fluid model for streamer discharges. Particular emphases will be placed on the correct implementation of transport data in streamer models as well as on the evaluation of the meanenergydependent collision rates for electrons required as an input in the highorder fluid model. In the last segment of this work, we will present our model to study the avalanche to streamer transition in nonpolar fluids. Using a Monte Carlo simulation technique we have calculated transport coefficients for electrons in
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)
Mishra, S. K.; Sahithi, V. V. D.; Rao, C. S. P.
20160901
The lot sizing problem deals with finding optimal order quantities which minimizes the ordering and holding cost of product mix. when multiple items at multiple levels with all capacity restrictions are considered, the lot sizing problem become NP hard. Many heuristics were developed in the past have inevitably failed due to size, computational complexity and time. However the authors were successful in the development of PSO based technique namely iterative improvement binary particles swarm technique to address very large capacitated multiitem multi level lot sizing (CMIMLLS) problem. First binary particle Swarm Optimization algorithm is used to find a solution in a reasonable time and iterative improvement local search mechanism is employed to improvise the solution obtained by BPSO algorithm. This hybrid mechanism of using local search on the global solution is found to improve the quality of solutions with respect to time thus IIBPSO method is found best and show excellent results.
Castellano, T.; De Palma, L.; Laneve, D.; Strippoli, V.; Cuccovilllo, A.; Prudenzano, F.; Dimiccoli, V.; Losito, O.; Prisco, R.
20150701
A homemade computer code for designing a Side Coupled Linear Accelerator (SCL) is written. It integrates a simplified model of SCL tanks with the Particle Swarm Optimization (PSO) algorithm. The computer code main aim is to obtain useful guidelines for the design of Linear Accelerator (LINAC) resonant cavities. The design procedure, assisted via the aforesaid approach seems very promising, allowing future improvements towards the optimization of actual accelerating geometries. (authors)
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.
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.
20170101
Consumers' opinions toward product design alternatives are often subjective and perceptual, which reflect their perception about a product and can be described using Kansei adjectives. Therefore, Kansei evaluation is often employed to determine consumers' preference. However, how to identify and improve the reliability of consumers' Kansei evaluation opinions toward design alternatives has an important role in adding additional insurance and reducing uncertainty to successful product design. To solve this problem, this study employs a consensus model to measure consistence among consumers' opinions, and an advanced particle swarm optimization (PSO) algorithm combined with Linearly Decreasing Inertia Weight (LDW) method is proposed for consensus reaching by minimizing adjustment of consumers' opinions. Furthermore, the process of the proposed method is presented and the details are illustrated using an example of electronic scooter design evaluation. The case study reveals that the proposed method is promising for reaching a consensus through searching optimal solutions by PSO and improving the reliability of consumers' evaluation opinions toward design alternatives according to Kansei indexes. PMID:28316619
NASA Astrophysics Data System (ADS)
Gerist, Saleheh; Maheri, Mahmoud R.
20161201
In order to solve structural damage detection problem, a multistage method using particle swarm optimization is presented. First, a new spars recovery method, named Basis Pursuit (BP), is utilized to preliminarily identify structural damage locations. The BP method solves a system of equations which relates the damage parameters to the structural modal responses using the sensitivity matrix. Then, the results of this stage are subsequently enhanced to the exact damage locations and extents using the PSO search engine. Finally, the search space is reduced by elimination of some low damage variables using micro search (MS) operator embedded in the PSO algorithm. To overcome the noise present in structural responses, a method known as Basis Pursuit DeNoising (BPDN) is also used. The efficiency of the proposed method is investigated by three numerical examples: a cantilever beam, a plane truss and a portal plane frame. The frequency response is used to detect damage in the examples. The simulation results demonstrate the accuracy and efficiency of the proposed method in detecting multiple damage cases and exhibit its robustness regarding noise and its advantages compared to other reported solution algorithms.
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)
Shan, Bonan; Wang, Jiang; Deng, Bin; Zhang, Zhen; Wei, Xile
20170301
Assessment of the effective connectivity among different brain regions during seizure is a crucial problem in neuroscience today. As a consequence, a new model inversion framework of brain function imaging is introduced in this manuscript. This framework is based on approximating brain networks using a multicoupled neural mass model (NMM). NMM describes the excitatory and inhibitory neural interactions, capturing the mechanisms involved in seizure initiation, evolution and termination. Particle swarm optimization method is used to estimate the effective connectivity variation (the parameters of NMM) and the epileptiform dynamics (the states of NMM) that cannot be directly measured using electrophysiological measurement alone. The estimated effective connectivity includes both the local connectivity parameters within a single region NMM and the remote connectivity parameters between multicoupled NMMs. When the epileptiform activities are estimated, a proportionalintegral controller outputs control signal so that the epileptiform spikes can be inhibited immediately. Numerical simulations are carried out to illustrate the effectiveness of the proposed framework. The framework and the results have a profound impact on the way we detect and treat epilepsy.
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
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.
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.
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.
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)
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.
Selfmodeling curve resolution (SMCR) by particle swarm optimization (PSO).
Shinzawa, Hideyuki; Jiang, JianHui; Iwahashi, Makio; Noda, Isao; Ozaki, Yukihiro
20070709
Particle swarm optimization (PSO) combined with alternating least squares (ALS) is introduced to selfmodeling curve resolution (SMCR) in this study for effective initial estimate. The proposed method aims to search concentration profiles or pure spectra which give the best resolution result by PSO. SMCR sometimes yields insufficient resolution results by getting trapped in a local minimum with poor initial estimates. The proposed method enables to reduce an undesirable effect of the local minimum in SMCR due to the advantages of PSO. Moreover, a new criterion based on global phase angle is also proposed for more effective performance of SMCR. It takes full advantage of data structure, that is to say, a sequential change with respect to a perturbation can be considered in SMCR with the criterion. To demonstrate its potential, SMCR by PSO is applied to concentrationdependent nearinfrared (NIR) spectra of mixture solutions of oleic acid (OA) and ethanol. Its curve resolution performances are compared with SMCR with evolving factor analysis (EFA). The results show that SMCR by PSO yields significantly better curve resolution performances than those by EFA. It is revealed that SMCR by PSO is less sensitive to a local minimum in SMCR and it can be a new effective tool for curve resolution analysis.
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)
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
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.
Hokari, Haruhide
20140101
Brainmachine interfaces (BMI) rely on the accurate classification of eventrelated potentials (ERPs) and their performance greatly depends on the appropriate selection of classifier parameters and features from densearray electroencephalography (EEG) signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA) and a multiobjective hybrid realbinary Particle Swarm Optimization (MHPSO) algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy. PMID:24982944
Wang, Yanchao; Miao, Maosheng; Lv, Jian; Zhu, Li; Yin, Ketao; Liu, Hanyu; Ma, Yanming
20121214
A structure prediction method for layered materials based on twodimensional (2D) particle swarm optimization algorithm is developed. The relaxation of atoms in the perpendicular direction within a given range is allowed. Additional techniques including structural similarity determination, symmetry constraint enforcement, and discretization of structure constructions based on space gridding are implemented and demonstrated to significantly improve the global structural search efficiency. Our method is successful in predicting the structures of known 2D materials, including single layer and multilayer graphene, 2D boron nitride (BN) compounds, and some quasi2D group 6 metals(VIB) chalcogenides. Furthermore, by use of this method, we predict a new family of monolayered boron nitride structures with different chemical compositions. The firstprinciples electronic structure calculations reveal that the band gap of these Nrich BN systems can be tuned from 5.40 eV to 2.20 eV by adjusting the composition.
CALIBRATION OF SEMIANALYTIC MODELS OF GALAXY FORMATION USING PARTICLE SWARM OPTIMIZATION
Ruiz, Andrés N.; Domínguez, Mariano J.; Yaryura, Yamila; Lambas, Diego García; Cora, Sofía A.; Martínez, Cristian A. Vega; Gargiulo, Ignacio D.; Padilla, Nelson D.; Tecce, Tomás E.; Orsi, Álvaro; Arancibia, Alejandra M. Muñoz
20150310
We present a fast and accurate method to select an optimal set of parameters in semianalytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a selflearning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semianalytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter Nbody simulation. The calibration is performed using a combination of observed galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the bestfitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.
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.
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
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.
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.
NASA Astrophysics Data System (ADS)
Pan, Hong; Xia, SiYu; Jin, LiZuo; Xia, LiangZheng
20111201
We propose a fast multiscale face detector that boosts a set of SVMbased hierarchy classifiers constructed with two heterogeneous features, i.e. Multiblock Local Binary Patterns (MBLBP) and Speeded Up Robust Features (SURF), at different image resolutions. In this hierarchical architecture, simple and fast classifiers using efficient MBLBP descriptors remove large parts of the background in low and intermediate scale layers, thus only a small percentage of background patches look similar to faces and require a more accurate but slower classifier that uses distinctive SURF descriptor to avoid false classifications in the finest scale. By propagating only those patterns that are not classified as background, we can quickly decrease the amount of data need to be processed. To lessen the training burden of the hierarchy classifier, in each scale layer, a feature selection scheme using Binary Particle Swarm Optimization (BPSO) searches the entire feature space and filters out the minimum number of discriminative features that give the highest classification rate on a validation set, then these selected distinctive features are fed into the SVM classifier. We compared detection performance of the proposed face detector with other stateoftheart methods on the CMU+MIT face dataset. Our detector achieves the best overall detection performance. The training time of our algorithm is 60 times faster than the standard Adaboost algorithm. It takes about 70 ms for our face detector to process a 320×240 image, which is comparable to Viola and Jones' detector.
Hybrid particle swarm global optimization algorithm for phase diversity phase retrieval.
Zhang, P G; Yang, C L; Xu, Z H; Cao, Z L; Mu, Q Q; Xuan, L
20161031
The core problem of phase diversity phase retrieval (PDPR) is to find suitable optimization algorithms for wavefront sensing of different scales, especially for largescale wavefront sensing. When dealing with largescale wavefront sensing, existing gradientbased local optimization algorithms used in PDPR are easily trapped in local minimums near initial positions, and available global optimization algorithms possess low convergence efficiency. We construct a practicable optimization algorithm used in PDPR for largescale wavefront sensing. This algorithm, named EPSOBFGS, is a twostep hybrid global optimization algorithm based on the combination of evolutionary particle swarm optimization (EPSO) and the BroydenFletcherGoldfarbShanno (BFGS) algorithm. Firstly, EPSO provides global search and obtains a rough global minimum position in limited search steps. Then, BFGS initialized by the rough global minimum position approaches the global minimum with high accuracy and fast convergence speed. Numerical examples testify to the feasibility and reliability of EPSOBFGS for wavefront sensing of different scales. Two numerical cases also validate the ability of EPSOBFGS for largescale wavefront sensing. The effectiveness of EPSOBFGS is further affirmed by performing a verification experiment.
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.
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.
Cheng, Xianfu; 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.
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.
NASA Astrophysics Data System (ADS)
Chen, Xi; Diez, Matteo; Kandasamy, Manivannan; Zhang, Zhiguo; Campana, Emilio F.; Stern, Frederick
20150401
Advances in highfidelity shape optimization for industrial problems are presented, based on geometric variability assessment and designspace dimensionality reduction by KarhunenLoève expansion, metamodels and deterministic particle swarm optimization (PSO). Hullform optimization is performed for resistance reduction of the highspeed Delft catamaran, advancing in calm water at a given speed, and free to sink and trim. Two feasible sets (A and B) are assessed, using different geometric constraints. Dimensionality reduction for 95% confidence is applied to highdimensional freeform deformation. Metamodels are trained by design of experiments with URANS; multiple deterministic PSOs achieve a resistance reduction of 9.63% for A and 6.89% for B. Deterministic PSO is found to be effective and efficient, as shown by comparison with stochastic PSO. The optimum for A has the best overall performance over a wide range of speed. Compared with earlier optimization, the present studies provide an additional resistance reduction of 6.6% at 1/10 of the computational cost.
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.
Superhard Fcarbon predicted by ab initio particleswarm optimization methodology.
Tian, Fei; Dong, Xiao; Zhao, Zhisheng; He, Julong; Wang, HuiTian
20120425
A simple (5 + 6 + 7)sp(3) carbon (denoted as Fcarbon) with eight atoms per unit cell predicted by a newly developed ab initio particleswarm optimization methodology on crystal structure prediction is proposed. Fcarbon can be seen as the reconstruction of AAstacked or 3Rgraphite, and is energetically more stable than 2Hgraphite beyond 13.9 GPa. Band structure and hardness calculations indicate that Fcarbon is a transparent superhard carbon with a gap of 4.55 eV at 15 GPa and a hardness of 93.9 GPa at zero pressure. Compared with the previously proposed Bct, M and Wcarbons, the simulative xray diffraction pattern of Fcarbon also well matches the superhard intermediate phase of the experimentally coldcompressed graphite. The possible transition route and energy barrier were observed using the variable cell nudged elastic band method. Our simulations show that the cold compression of graphite can produce some reversible metastable carbons (e.g. M and Fcarbons) with energy barriers close to diamond or lonsdaleite.
Toeplitz block circulant matrix optimized with particle swarm optimization for compressive imaging
NASA Astrophysics Data System (ADS)
Tao, Huifeng; Yin, Songfeng; Tang, Cong
20161001
Compressive imaging is an imaging way based on the compressive sensing theory, which could achieve to capture the high resolution image through a small set of measurements. As the core of the compressive imaging, the design of the measurement matrix is sufficient to ensure that the image can be recovered from the measurements. Due to the fast computing capacity and the characteristic of easy hardware implementation, The Toeplitz block circulant matrix is proposed to realize the encoded samples. The measurement matrix is usually optimized for improving the image reconstruction quality. However, the existing optimization methods can destroy the matrix structure easily when applied to the Toeplitz block circulant matrix optimization process, and the deterministic iterative processes of them are inflexible, because of requiring the task optimized to need to satisfy some certain mathematical property. To overcome this problem, a novel method of optimizing the Toeplitz block circulant matrix based on the particle swarm optimization intelligent algorithm is proposed in this paper. The objective function is established by the way of approaching the target matrix that is the Gram matrix truncated by the Welch threshold. The optimized object is the vector composed by the free entries instead of the Gram matrix. The experimental results indicate that the Toeplitz block circulant measurement matrix can be optimized while preserving the matrix structure by our method, and result in the reconstruction quality improvement.
NASA Astrophysics Data System (ADS)
Wu, LiLi; Zhou, Qihou H.; Chen, TieJun; Liang, J. J.; Wu, Xin
20150901
Simultaneous derivation of multiple ionospheric parameters from the incoherent scatter power spectra in the F1 region is difficult because the spectra have only subtle differences for different combinations of parameters. In this study, we apply a particle swarm optimizer (PSO) to incoherent scatter power spectrum fitting and compare it to the commonly used least squares fitting (LSF) technique. The PSO method is found to outperform the LSF method in practically all scenarios using simulated data. The PSO method offers the advantages of not being sensitive to initial assumptions and allowing physical constraints to be easily built into the model. When simultaneously fitting for molecular ion fraction (fm), ion temperature (Ti), and ratio of ion to electron temperature (γT), γT is largely stable. The uncertainty between fm and Ti can be described as a quadratic relationship. The significance of this result is that Ti can be retroactively corrected for data archived many years ago where the assumption of fm may not be accurate, and the original power spectra are unavailable. In our discussion, we emphasize the fitting for fm, which is a difficult parameter to obtain. PSO method is often successful in obtaining fm, whereas LSF fails. We apply both PSO and LSF to actual observations made by the Arecibo incoherent scatter radar. The results show that PSO method is a viable method to simultaneously determine ion and electron temperatures and molecular ion fraction when the last is greater than 0.3.
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.
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.
Zhang, Yong; Gong, DunWei; Cheng, Jian
20170101
Feature selection is an important datapreprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called costbased feature selection. However, most existing feature selection approaches treat this task as a singleobjective optimization problem. This paper presents the first study of multiobjective particle swarm optimization (PSO) for costbased feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decisionmakers in realworld applications. In order to enhance the search capability of the proposed algorithm, a probabilitybased encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSObased multiobjective feature selection algorithm is compared with several multiobjective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving costbased feature selection problems.
Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider
20160101
Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a MultiObjective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based preprocessing technique is employed to reduce some of the crude domain features from the initial feature set. Since these preprocessed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multiobjective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm.
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.
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.
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.
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. PMID:25815046
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.
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.
3D gravity inversion and uncertainty assessment of basement relief via Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Pallero, J. L. G.; FernándezMartínez, J. L.; Bonvalot, S.; Fudym, O.
20170401
Nonlinear gravity inversion in sedimentary basins is a classical problem in applied geophysics. Although a 2D approximation is widely used, 3D models have been also proposed to better take into account the basin geometry. A common nonlinear approach to this 3D problem consists in modeling the basin as a set of right rectangular prisms with prescribed density contrast, whose depths are the unknowns. Then, the problem is iteratively solved via local optimization techniques from an initial model computed using some simplifications or being estimated using prior geophysical models. Nevertheless, this kind of approach is highly dependent on the prior information that is used, and lacks from a correct solution appraisal (nonlinear uncertainty analysis). In this paper, we use the family of global Particle Swarm Optimization (PSO) optimizers for the 3D gravity inversion and model appraisal of the solution that is adopted for basement relief estimation in sedimentary basins. Synthetic and real cases are illustrated, showing that robust results are obtained. Therefore, PSO seems to be a very good alternative for 3D gravity inversion and uncertainty assessment of basement relief when used in a sampling while optimizing approach. That way important geological questions can be answered probabilistically in order to perform risk assessment in the decisions that are made.
Yang, YanPu
20170101
Consumers' opinions toward product design alternatives are often subjective and perceptual, which reflect their perception about a product and can be described using Kansei adjectives. Therefore, Kansei evaluation is often employed to determine consumers' preference. However, how to identify and improve the reliability of consumers' Kansei evaluation opinions toward design alternatives has an important role in adding additional insurance and reducing uncertainty to successful product design. To solve this problem, this study employs a consensus model to measure consistence among consumers' opinions, and an advanced particle swarm optimization (PSO) algorithm combined with Linearly Decreasing Inertia Weight (LDW) method is proposed for consensus reaching by minimizing adjustment of consumers' opinions. Furthermore, the process of the proposed method is presented and the details are illustrated using an example of electronic scooter design evaluation. The case study reveals that the proposed method is promising for reaching a consensus through searching optimal solutions by PSO and improving the reliability of consumers' evaluation opinions toward design alternatives according to Kansei indexes.
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
NASA Astrophysics Data System (ADS)
Shan, Bonan; Wang, Jiang; Zhang, Lvxia; Deng, Bin; Wei, Xile
20170201
In order to fit neural model’s spiking features to electrophysiological recordings, in this paper, a fitting framework based on particle swarm optimization (PSO) algorithm is proposed to estimate the model parameters in an augmented multitimescale adaptive threshold (AugMAT) model. PSO algorithm is an advanced evolutionary calculation method based on iteration. Selecting a reasonable criterion function will ensure the effectiveness of PSO algorithm. In this work, firing rate information is used as the main spiking feature and the estimation error of firing rate is selected as the criterion for fitting. A series of simulations are presented to verify the performance of the framework. The first step is model validation; an artificial training data is introduced to test the fitting procedure. Then we talk about the suitable PSO parameters, which exhibit adequate compromise between speediness and accuracy. Lastly, this framework is used to fit the electrophysiological recordings, after three adjustment steps, the features of experimental data are translated into realistic spiking neuron model.
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.
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
Hybrid quantum systems with trapped charged particles
NASA Astrophysics Data System (ADS)
Kotler, Shlomi; Simmonds, Raymond W.; Leibfried, Dietrich; Wineland, David J.
20170201
Trapped charged particles have been at the forefront of quantum information processing (QIP) for a few decades now, with deterministic twoqubit logic gates reaching record fidelities of 99.9 % and singlequbit operations of much higher fidelity. In a hybrid system involving trapped charges, quantum degrees of freedom of macroscopic objects such as bulk acoustic resonators, superconducting circuits, or nanomechanical membranes, couple to the trapped charges and ideally inherit the coherent properties of the charges. The hybrid system therefore implements a "quantum transducer," where the quantum reality (i.e., superpositions and entanglement) of small objects is extended to include the larger object. Although a hybrid quantum system with trapped charges could be valuable both for fundamental research and for QIP applications, no such system exists today. Here we study theoretically the possibilities of coupling the quantummechanical motion of a trapped charged particle (e.g., an ion or electron) to the quantum degrees of freedom of superconducting devices, nanomechanical resonators, and quartz bulk acoustic wave resonators. For each case, we estimate the coupling rate between the charged particle and its macroscopic counterpart and compare it to the decoherence rate, i.e., the rate at which quantum superposition decays. A hybrid system can only be considered quantum if the coupling rate significantly exceeds all decoherence rates. Our approach is to examine specific examples by using parameters that are experimentally attainable in the foreseeable future. We conclude that hybrid quantum systems involving a single atomic ion are unfavorable compared with the use of a single electron because the coupling rates between the ion and its counterpart are slower than the expected decoherence rates. A system based on trapped electrons, on the other hand, might have coupling rates that significantly exceed decoherence rates. Moreover, it might have appealing properties such
Device and programming abstractions for spatiotemporal control of active microparticle swarms.
Lam, Amy T; SamuelGama, Karina G; Griffin, Jonathan; Loeun, Matthew; Gerber, Lukas C; Hossain, Zahid; Cira, Nate J; Lee, Seung Ah; RiedelKruse, Ingmar H
20170321
We present a hardware setup and a set of executable commands for spatiotemporal programming and interactive control of a swarm of selfpropelled microscopic agents inside a microfluidic chip. In particular, local and global spatiotemporal light stimuli are used to direct the motion of ensembles of Euglena gracilis, a unicellular phototactic organism. We develop three levels of programming abstractions (stimulus space, swarm space, and system space) to create a scripting language for directing swarms. We then implement a multilevel proofofconcept biotic game using these commands to demonstrate their utility. These device and programming concepts will enhance our capabilities for manipulating natural and synthetic swarms, with future applications for onchip processing, diagnostics, education, and research on collective behaviors.
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.
Quantum evaporation of flavormixed particles
NASA Astrophysics Data System (ADS)
Medvedev, Mikhail V.
20140301
Particles whose propagation (mass) and interaction (flavor) bases are misaligned are mixed, e.g., neutrinos, quarks, Kaons, etc. We show that interactions (elastic scattering) of individual masseigenstates can result in their interconversions. Most intriguing and counterintuitive implication of this process is a new process, which we refer to as the ``quantum evaporation.'' Consider a mixed particle trapped in a gravitational potential. If such a particle scatters off something (e.g., from another mixed particle) elastically from time to time, this particle (or both particles, respectively) can eventually escape to infinity with no extra energy supplied. That is as if a ``flavormixed satellite'' hauled along a bumpy road puts itself in space without a rocket, fuel, etc. Of course, the process at hand is entirely quantum and has no counterpart in classical mechanics. It also has nothing to do with tunneling or other known processes. We discuss some implications to the dark matter physics, cosmology and cosmic neutrino background. Supported by grant DOE grant DEFG0207ER54940 and NSF grant AST1209665.
Mohamad, Mohd Saberi; Omatu, Sigeru; Deris, Safaai; Yoshioka, Michifumi
20111101
Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.
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
Dirac particle in gravitational quantum mechanics
NASA Astrophysics Data System (ADS)
Pedram, Pouria
20110801
In this Letter, we consider the effects of the Generalized (Gravitational) Uncertainty Principle (GUP) on the eigenvalues and the eigenfunctions of the Dirac equation. This form of GUP is consistent with various candidates of quantum gravity such as string theory, loop quantum gravity, doubly special relativity and black hole physics and predicts both a minimum measurable length and a maximum measurable momentum. The modified Hamiltonian contains two additional terms proportional to a( and a( where αi are Dirac matrices and a∼1/MPlc is the GUP parameter. For the case of the Dirac free particle and the Dirac particle in a box, we solve the generalized Dirac equation and find the modified energy eigenvalues and eigenfunctions.
NASA Astrophysics Data System (ADS)
Li, Duan; Xu, Lijun; Li, Xiaolu
20170401
To measure the distances and properties of the objects within a laser footprint, a decomposition method for fullwaveform light detection and ranging (LiDAR) echoes is proposed. In this method, firstly, wavelet decomposition is used to filter the noise and estimate the noise level in a fullwaveform echo. Secondly, peak and inflection points of the filtered fullwaveform echo are used to detect the echo components in the filtered fullwaveform echo. Lastly, particle swarm optimization (PSO) is used to remove the noisecaused echo components and optimize the parameters of the most probable echo components. Simulation results show that the waveletdecompositionbased filter is of the best improvement of SNR and decomposition success rates than Wiener and Gaussian smoothing filters. In addition, the noise level estimated using waveletdecompositionbased filter is more accurate than those estimated using other two commonly used methods. Experiments were carried out to evaluate the proposed method that was compared with our previous method (called GSLM for short). In experiments, a labbuild fullwaveform LiDAR system was utilized to provide eight types of fullwaveform echoes scattered from three objects at different distances. Experimental results show that the proposed method has higher success rates for decomposition of fullwaveform echoes and more accurate parameters estimation for echo components than those of GSLM. The proposed method based on wavelet decomposition and PSO is valid to decompose the more complicated fullwaveform echoes for estimating the multilevel distances of the objects and measuring the properties of the objects in a laser footprint.
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.
Particle swarm optimization for feature selection in classification: a multiobjective approach.
Xue, Bing; Zhang, Mengjie; Browne, Will N
20131201
Classification problems often have a large number of features in the data sets, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the performance. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maximizing the classification performance and minimizing the number of features. However, most existing feature selection algorithms treat the task as a single objective problem. This paper presents the first study on multiobjective particle swarm optimization (PSO) for feature selection. The task is to generate a Pareto front of nondominated solutions (feature subsets). We investigate two PSObased multiobjective feature selection algorithms. The first algorithm introduces the idea of nondominated sorting into PSO to address feature selection problems. The second algorithm applies the ideas of crowding, mutation, and dominance to PSO to search for the Pareto front solutions. The two multiobjective algorithms are compared with two conventional feature selection methods, a single objective feature selection method, a twostage feature selection algorithm, and three wellknown evolutionary multiobjective algorithms on 12 benchmark data sets. The experimental results show that the two PSObased multiobjective algorithms can automatically evolve a set of nondominated solutions. The first algorithm outperforms the two conventional methods, the single objective method, and the twostage algorithm. It achieves comparable results with the existing three wellknown multiobjective algorithms in most cases. The second algorithm achieves better results than the first algorithm and all other methods mentioned previously.
NASA Astrophysics Data System (ADS)
Abedi, Kambiz; Mirjalili, Seyed Mohammad
20150301
Recently, majority of current research in the field of designing Phonic Crystal Waveguides (PCW) focus in extracting the relations between output slow light properties of PCW and structural parameters through a huge number of tedious nonsystematic simulations in order to introduce better designs. This paper proposes a novel systematic approach which can be considered as a shortcut to alleviate the difficulties and human involvements in designing PCWs. In the proposed method, the problem of PCW design is first formulated as an optimization problem. Then, an optimizer is employed in order to automatically find the optimum design for the formulated PCWs. Meanwhile, different constraints are also considered during optimization with the purpose of applying physical limitations to the final optimum structure. As a case study, the structure of a Bragglike Corrugation Slotted PCWs (BCSPCW) is optimized by using the proposed method. One of the most computationally powerful techniques in Computational Intelligence (CI) called Particle Swarm Optimization (PSO) is employed as an optimizer to automatically find the optimum structure for BCSPCW. The optimization process is done by considering five constraints to guarantee the feasibility of the final optimized structures and avoid band mixing. Numerical results demonstrate that the proposed method is able to find an optimum structure for BCSPCW with 172% and 100% substantial improvements in the bandwidth and Normalized DelayBandwidth Product (NDBP) respectively compared to the best current structure in the literature. Moreover, there is a time domain analysis at the end of the paper which verifies the performance of the optimized structure and proves that this structure has low distortion and attenuation simultaneously.
Chai, Rifai; Ling, Sai Ho; Hunter, Gregory P; Tran, Yvonne; Nguyen, Hung T
20140901
This paper presents the classification of a threeclass mental taskbased braincomputer interface (BCI) that uses the HilbertHuang transform for the features extractor and fuzzy particle swarm optimization with crossmutatedbased artificial neural network (FPSOCMANN) for the classifier. The experiments were conducted on five ablebodied subjects and five patients with tetraplegia using electroencephalography signals from six channels, and different timewindows of data were examined to find the highest accuracy. For practical purposes, the best two channel combinations were chosen and presented. The three relevant mental tasks used for the BCI were letter composing, arithmetic, and Rubik's cube rolling forward, and these are associated with three wheelchair commands: left, right, and forward, respectively. An additional eyes closed task was collected for testing and used for onoff commands. The results show a dominant alpha wave during eyes closure with average classification accuracy above 90%. The accuracies for patients with tetraplegia were lower compared to the ablebodied subjects; however, this was improved by increasing the duration of the timewindows. The FPSOCMANN provides improved accuracies compared to genetic algorithmbased artificial neural network (GAANN) for three mental tasksbased BCI classifications with the best classification accuracy achieved for a 7s timewindow: 84.4% (FPSOCMANN) compared to 77.4% (GAANN). More comparisons on feature extractors and classifiers were included. For twochannel classification, the best two channels were O1 and C4, followed by second best at P3 and O2, and third best at C3 and O2. Mental arithmetic was the most correctly classified task, followed by mental Rubik's cube rolling forward and mental letter composing.
Effective Particles in Quantum Field Theory
NASA Astrophysics Data System (ADS)
Głazek, Stanisław D.; Trawiński, Arkadiusz P.
20170301
The concept of effective particles is introduced in the Minkowski spacetime Hamiltonians in quantum field theory using a new kind of the relativistic renormalization group procedure that does not integrate out highenergy modes but instead integrates out the large changes of invariant mass. The new procedure is explained using examples of known interactions. Some applications in phenomenology, including processes measurable in colliders, are briefly presented.
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.
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.
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.
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.
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
Li, Dongsheng; Yang, Wei; Zhang, Wenyao
20170501
Stress corrosion is the major failure type of bridge cable damage. The acoustic emission (AE) technique was applied to monitor the stress corrosion process of steel wires used in bridge cable structures. The damage evolution of stress corrosion in bridge cables was obtained according to the AE characteristic parameter figure. A particle swarm optimization cluster method was developed to determine the relationship between the AE signal and stress corrosion mechanisms. Results indicate that the main AE sources of stress corrosion in bridge cables included four types: passive film breakdown and detachment of the corrosion product, crack initiation, crack extension, and cable fracture. By analyzing different types of clustering data, the mean value of each damage pattern's AE characteristic parameters was determined. Different corrosion damage source AE waveforms and the peak frequency were extracted. AE particle swarm optimization cluster analysis based on principal component analysis was also proposed. This method can completely distinguish the four types of damage sources and simplifies the determination of the evolution process of corrosion damage and broken wire signals.
Huang, Daizheng; Wu, Zhihui
20170101
Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and backpropagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a threelayer backpropagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of backpropagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods. PMID:28222194
Huang, Daizheng; Wu, Zhihui
20170101
Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and backpropagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a threelayer backpropagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of backpropagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.
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)
Djeffal, F.; Lakhdar, N.; Meguellati, M.; Benhaya, A.
20090901
The analytical modeling of electron mobility in wurtzite Gallium Nitride (GaN) requires several simplifying assumptions, generally necessary to lead to compact expressions of electron transport characteristics for GaNbased devices. Further progress in the development, design and optimization of GaNbased devices necessarily requires new theory and modeling tools in order to improve the accuracy and the computational time of devices simulators. Recently, the evolutionary techniques, genetic algorithms ( GA) and particle swarm optimization ( PSO), have attracted considerable attention among various heuristic optimization techniques. In this paper, a particle swarm optimizer is implemented and compared to a genetic algorithm for modeling and optimization of new closed electron mobility model for GaNbased devices design. The performance of both optimization techniques in term of computational time and convergence rate is also compared. Further, our obtained results for both techniques ( PSO and GA) are tested and compared with numerical data (Monte Carlo simulations) where a good agreement has been found for wide range of temperature, doping and applied electric field. The developed analytical models can also be incorporated into the circuits simulators to study GaNbased devices without impact on the computational time and data storage.
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
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.
Exceptional quantum geometry and particle physics
NASA Astrophysics Data System (ADS)
DuboisViolette, Michel
20161101
Based on an interpretation of the quarklepton symmetry in terms of the unimodularity of the color group SU (3) and on the existence of 3 generations, we develop an argumentation suggesting that the "finite quantum space" corresponding to the exceptional real Jordan algebra of dimension 27 (the Euclidean Albert algebra) is relevant for the description of internal spaces in the theory of particles. In particular, the triality which corresponds to the 3 offdiagonal octonionic elements of the exceptional algebra is associated to the 3 generations of the Standard Model while the representation of the octonions as a complex 4dimensional space C ⊕C3 is associated to the quarklepton symmetry (one complex for the lepton and 3 for the corresponding quark). More generally it is suggested that the replacement of the algebra of real functions on spacetime by the algebra of functions on spacetime with values in a finitedimensional Euclidean Jordan algebra which plays the role of "the algebra of real functions" on the corresponding almost classical quantum spacetime is relevant in particle physics. This leads us to study the theory of Jordan modules and to develop the differential calculus over Jordan algebras (i.e. to introduce the appropriate notion of differential forms). We formulate the corresponding definition of connections on Jordan modules.
NASA Astrophysics Data System (ADS)
Armbruster, Dieter; Motsch, Sébastien; Thatcher, Andrea
20170401
The Vicsek model is a prototype for the emergence of collective motion. In free space, it is characterized by a swarm of particles all moving in the same direction. Since this dynamic does not include attraction among particles, the swarm, while aligning in velocity space, has no spatial coherence. Adding specular reflection at the boundaries generates global spatial coherence of the swarms while maintaining its velocity alignment. We investigate numerically how the geometry of the domain influences the Vicsek model using three type of geometry: a channel, a disk and a rectangle. Varying the parameters of the Vicsek model (e.g. noise levels and influence horizons), we discuss the mechanisms that generate spatial coherence and show how they create new dynamical solutions of the swarming motions in these geometries. Several observables are introduced to characterize the simulated patterns (e.g. mass profile, center of mass, connectivity of the swarm).
NASA Astrophysics Data System (ADS)
Guo, Guodong; Hackney, Drew; Pankow, Mark; Peters, Kara
20170401
This paper applies the concept of spectral profile division multiplexing to track each Bragg wavelength shift in a serially multiplexed fiber Bragg grating (FBG) network. Each sensor in the network is uniquely characterized by its own reflected spectrum shape, thus spectral overlapping is allowed in the wavelength domain. In contrast to the previous literature, spectral distortion caused by multiple reflections and spectral shadowing between FBG sensors, that occur in serial topology sensor networks, are considered in the identification algorithm. To detect the Bragg wavelength shift of each FBG, a nonlinear optimization function based on the output spectrum is constructed and a modified dynamic multiswarm particle swarm optimizer is employed. The multiplexing approach is experimentally demonstrated on data from multiplexed sensor networks with up to four sensors. The wavelength prediction results show that the method can efficiently interrogate the multiplexed network in these overlapped situations. Specifically, the maximum error in a fully overlapped situation in the specific four sensor network demonstrated here was only 110 pm. A more general analysis of the prediction error and guidelines to optimize the sensor network are the subject of future work.
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.
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
A quantum particle in a box with moving walls
NASA Astrophysics Data System (ADS)
Di Martino, Sara; Anzà, Fabio; Facchi, Paolo; Kossakowski, Andrzej; Marmo, Giuseppe; Messina, Antonino; Militello, Benedetto; Pascazio, Saverio
20130901
We analyze the nonrelativistic problem of a quantum particle that bounces back and forth between two moving walls. We recast this problem into the equivalent one of a quantum particle in a fixed box whose dynamics is governed by an appropriate timedependent Schrödinger operator.
Switchable particle statistics with an embedding quantum simulator
NASA Astrophysics Data System (ADS)
Cheng, XiaoHang; Arrazola, Iñigo; Pedernales, Julen S.; Lamata, Lucas; Chen, Xi; Solano, Enrique
20170201
We propose the implementation of a switch of particle statistics with an embedding quantum simulator. By encoding both BoseEinstein and FermiDirac statistics into an enlarged Hilbert space, the statistics of the simulated quantum particles may be changed in situ during the time evolution, from bosons to fermions and from fermions to bosons, as many times as desired before a measurement is performed. We illustrate our proposal with fewqubit examples, although the protocol is straightforwardly extendable to larger numbers of particles. This proposal can be implemented on different quantum platforms such as trapped ions, quantum photonics, and superconducting circuits, among others. The possibility to implement permutation symmetrization and antisymmetrization of quantum particles enhances the toolbox of quantum simulations for unphysical operations as well as for symmetry transformations.
Motivating quantum field theory: the boosted particle in a box
NASA Astrophysics Data System (ADS)
Vutha, Amar C.
20130701
It is a maxim often stated, yet rarely illustrated, that the combination of special relativity and quantum mechanics necessarily leads to quantum field theory. An elementary illustration is provided using the familiar particle in a box, boosted to relativistic speeds. It is shown that quantum fluctuations of momentum lead to energy fluctuations, which are inexplicable without a framework that endows the vacuum with dynamical degrees of freedom and allows particle creation/annihilation.
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.
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.
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.
Zhang, YongFeng; Chiang, HsiaoDong
20160620
A novel threestage methodology, termed the "consensusbased particle swarm optimization (PSO)assisted TrustTech methodology," to find global optimal solutions for nonlinear optimization problems is presented. It is composed of TrustTech methods, consensusbased PSO, and local optimization methods that are integrated to compute a set of highquality local optimal solutions that can contain the global optimal solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of smalldimension benchmark optimization problems and 20 largedimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain highquality optimal solutions that can contain the global optimal solution. The scalability of the proposed methodology is promising.
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.
NASA Astrophysics Data System (ADS)
Ghanei, A.; Assareh, E.; Biglari, M.; Ghanbarzadeh, A.; Noghrehabadi, A. R.
20141001
Many studies are performed by researchers about shell and tube heat exchanger (STHE) but the multiobjective particle swarm optimization (PSO) technique has never been used in such studies. This paper presents application of thermaleconomic multiobjective optimization of STHE using PSO. For optimal design of a STHE, it was first thermally modeled using enumber of transfer units method while BellDelaware procedure was applied to estimate its shell side heat transfer coefficient and pressure drop. Multi objective PSO (MOPSO) method was applied to obtain the maximum effectiveness (heat recovery) and the minimum total cost as two objective functions. The results of optimal designs were a set of multiple optimum solutions, called `Pareto optimal solutions'. In order to show the accuracy of the algorithm, a comparison is made with the nondominated sorting genetic algorithm (NSGAII) and MOPSO which are developed for the same problem.
NASA Astrophysics Data System (ADS)
Chen, YuRen; Dye, ChungYuan
20130601
In most of the inventory models in the literature, the deterioration rate of goods is viewed as an exogenous variable, which is not subject to control. In the real market, the retailer can reduce the deterioration rate of product by making effective capital investment in storehouse equipments. In this study, we formulate a deteriorating inventory model with timevarying demand by allowing preservation technology cost as a decision variable in conjunction with replacement policy. The objective is to find the optimal replenishment and preservation technology investment strategies while minimising the total cost over the planning horizon. For any given feasible replenishment scheme, we first prove that the optimal preservation technology investment strategy not only exists but is also unique. Then, a particle swarm optimisation is coded and used to solve the nonlinear programming problem by employing the properties derived from this article. Some numerical examples are used to illustrate the features of the proposed model.
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.
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.
MultipleParticle Interference and Quantum Error Correction
NASA Astrophysics Data System (ADS)
Steane, Andrew
19961101
The concept of multipleparticle interference is discussed, using insights provided by the classical theory of error correcting codes. This leads to a discussion of error correction in a quantum communication channel or a quantum computer. Methods of error correction in the quantum regime are presented, and their limitations assessed. A quantum channel can recover from arbitrary decoherence of x qubits if K bits of quantum information are encoded using n quantum bits, where K/n can be greater than 1  2H (2x/n), but must be less than 1  2H (x/n). This implies exponential reduction of decoherence with only a polynomial increase in the computing resources required. Therefore quantum computation can be made free of errors in the presence of physically realistic levels of decoherence. The methods also allow isolation of quantum communication from noise and evesdropping (quantum privacy amplification).
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.
NASA Astrophysics Data System (ADS)
Hu, Yifan; Ding, Yongsheng; Hao, Kuangrong; Ren, Lihong; Han, Hua
20140301
The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bioheuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOAbased routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOAbased protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.
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%.
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.
Dujko, S; White, R D; Petrović, Z Lj; Robson, R E
20100401
A multiterm solution of the Boltzmann equation has been developed and used to calculate transport coefficients of chargedparticle swarms in gases under the influence of electric and magnetic fields crossed at arbitrary angles when nonconservative collisions are present. The hierarchy resulting from a sphericalharmonic decomposition of the Boltzmann equation in the hydrodynamic regime is solved numerically by representing the speed dependence of the phasespace distribution function in terms of an expansion in Sonine polynomials about a Maxwellian velocity distribution at an internally determined temperature. Results are given for electron swarms in certain collisional models for ionization and attachment over a range of angles between the fields and field strengths. The implicit and explicit effects of ionization and attachment on the electrontransport coefficients are considered using physical arguments. It is found that the difference between the two sets of transport coefficients, bulk and flux, resulting from the explicit effects of nonconservative collisions, can be controlled either by the variation in the magnetic field strengths or by the angles between the fields. In addition, it is shown that the phenomena of ionization cooling and/or attachment cooling/heating previously reported for dc electric fields carry over directly to the crossed electric and magnetic fields. The results of the Boltzmann equation analysis are compared with those obtained by a Monte Carlo simulation technique. The comparison confirms the theoretical basis and numerical integrity of the moment method for solving the Boltzmann equation and gives a set of wellestablished data that can be used to test future codes and plasma models.
Nonequilibrium of charged particles in swarms and plasmas—from binary collisions to plasma effects
NASA Astrophysics Data System (ADS)
Petrović, Z. Lj; Simonović, I.; Marjanović, S.; Bošnjaković, D.; Marić, D.; Malović, G.; Dujko, S.
20170101
In this article we show three quite different examples of lowtemperature plasmas, where one can follow the connection of the elementary binary processes (occurring at the nanoscopic scale) to the macroscopic discharge behavior and to its application. The first example is on the nature of the higherorder transport coefficient (secondorder diffusion or skewness); how it may be used to improve the modelling of plasmas and also on how it may be used to discern details of the relevant cross sections. A prerequisite for such modeling and use of transport data is that the hydrodynamic approximation is applicable. In the second example, we show the actual development of avalanches in a resistive plate chamber particle detector by conducting kinetic modelling (although it may also be achieved by using swarm data). The current and deposited charge waveforms may be predicted accurately showing temporal resolution, which allows us to optimize detectors by adjusting the gas mixture composition and external fields. Here kinetic modeling is necessary to establish high accuracy and the details of the physics that supports fluid models that allows us to follow the transition to streamers. Finally, we show an example of positron traps filled with gas that, for all practical purposes, are a weakly ionized gas akin to swarms, and may be modelled in that fashion. However, low pressures dictate the need to apply full kinetic modelling and use the energy distribution function to explain the kinetics of the system. In this way, it is possible to confirm a well established phenomenology, but in a manner that allows precise quantitative comparisons and description, and thus open doors to a possible optimization.
Singleparticle machine for quantum thermalization
Liao Jieqiao; Dong, H.; Sun, C. P.
20100515
The long time accumulation of the random actions of a single particle 'reservoir' on its coupled system can transfer some temperature information of its initial state to the coupled system. This dynamic process can be referred to as a quantum thermalization in the sense that the coupled system can reach a stable thermal equilibrium with a temperature equal to that of the reservoir. We illustrate this idea based on the usual micromaser model, in which a series of initially prepared twolevel atoms randomly pass through an electromagnetic cavity. It is found that, when the randomly injected atoms are initially prepared in a thermal equilibrium state with a given temperature, the cavity field will reach a thermal equilibrium state with the same temperature as that of the injected atoms. As in two limit cases, the cavity field can be cooled and 'coherently heated' as a maser process, respectively, when the injected atoms are initially prepared in ground and excited states. Especially, when the atoms in equilibrium are driven to possess some coherence, the cavity field may reach a higher temperature in comparison with the injected atoms. We also point out a possible experimental test for our theoretical prediction based on a superconducting circuit QED 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.
NASA Astrophysics Data System (ADS)
Wang, Guanghui; Chen, Jie; Cai, Tao; Xin, Bin
20130901
This article proposes a decompositionbased multiobjective differential evolution particle swarm optimization (DMDEPSO) algorithm for the design of a tubular permanent magnet linear synchronous motor (TPMLSM) which takes into account multiple conflicting objectives. In the optimization process, the objectives are evaluated by an artificial neural network response surface (ANNRS), which is trained by the samples of the TPMSLM whose performances are calculated by finite element analysis (FEA). DMDEPSO which hybridizes differential evolution (DE) and particle swarm optimization (PSO) together, first decomposes the multiobjective optimization problem into a number of singleobjective optimization subproblems, each of which is associated with a Pareto optimal solution, and then optimizes these subproblems simultaneously. PSO updates the position of each particle (solution) according to the best information about itself and its neighbourhood. If any particle stagnates continuously, DE relocates its position by using two different particles randomly selected from the whole swarm. Finally, based on the DMDEPSO, optimization is gradually carried out to maximize the thrust of TPMLSM and minimize the ripple, permanent magnet volume, and winding volume simultaneously. The result shows that the optimized TPMLSM meets or exceeds the performance requirements. In addition, comparisons with chosen algorithms illustrate the effectiveness of DMDEPSO to find the Pareto optimal solutions for the TPMLSM optimization problem.
Microfluidic generation of multifunctional quantum dot barcode particles.
Zhao, Yuanjin; Shum, Ho Cheung; Chen, Haosheng; Adams, Laura L A; Gu, Zhongze; Weitz, David A
20110615
We develop a new strategy to prepare quantum dot (QD) barcode particles by polymerizing doubleemulsion droplets prepared in capillary microfluidic devices. The resultant barcode particles are composed of stable QDtagged core particles surrounded by hydrogel shells. These particles exhibit uniform spectral characteristics and excellent coding capability, as confirmed by photoluminescence analyses. By using doubleemulsion droplets with two inner droplets of distinct phases as templates, we have also fabricated anisotropic magnetic barcode particles with two separate cores or with a Janus core. These particles enable optical encoding and magnetic separation, thus making them excellent functional barcode particles in biomedical applications.
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)
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
Size and temperature dependent plasmons of quantum particles
NASA Astrophysics Data System (ADS)
Xiao, Mufei; Rakov, Nikifor
20150801
This work reports on the influences of temperature changes on plasmons of metallic particles that are so small that electric carriers in the conduction band are forced to be at discrete subbands due to quantum confinement. In the framework of the electroninabox model and with an everyelectroncount computational scheme, the spatial electric distribution inside the particle is calculated. In the calculations, the intrasubband fluctuations are taken into account. The numerical results have shown that the smallparticle plasmon frequency shifts with the temperature. The findings suggest that it would be possible to control the plasmons of quantum particles externally.
Evolving optimised decision rules for intrusion detection using particle swarm paradigm
NASA Astrophysics Data System (ADS)
Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.
20121201
The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machinelearning paradigm enhances the detection accuracy of IDS. In this article, a rulebased approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisationbased approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machinelearning algorithm.
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.
Elastic and inelastic collisions of swarms
NASA Astrophysics Data System (ADS)
Armbruster, Dieter; Martin, Stephan; Thatcher, Andrea
20170401
Scattering interactions of swarms in potentials that are generated by an attractionrepulsion model are studied. In free space, swarms in this model form a welldefined steady state describing the translation of a stable formation of the particles whose shape depends on the interaction potential. Thus, the collision between a swarm and a boundary or between two swarms can be treated as (quasi)particle scattering. Such scattering experiments result in internal excitations of the swarm or in bound states, respectively. In addition, varying a parameter linked to the relative importance of damping and potential forces drives transitions between elastic and inelastic scattering of the particles. By tracking the swarm's center of mass, a refraction rule is derived via simulations relating the incoming and outgoing directions of a swarm hitting the wall. Iterating the map derived from the refraction law allows us to predict and understand the dynamics and bifurcations of swarms in square boxes and in channels.
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
20051104
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.
NASA Astrophysics Data System (ADS)
Lattuada, Enrico; Buzzaccaro, Stefano; Piazza, Roberto
20160101
By experimenting on model colloids where depletion forces can be carefully tuned and quantified, we show that attractive interactions consistently "promote" particle settling, so much that the sedimentation velocity of a moderately concentrated dispersion can even exceed its singleparticle value. At larger particle volume fraction ϕ , however, hydrodynamic hindrance eventually takes over. Hence, v (ϕ ) actually displays a nonmonotonic trend that may threaten the stability of the settling front to thermal perturbations. Finally, by discussing a representative case, we show that these results are relevant to the investigation of protein association effects by ultracentrifugation.
Authenticated multiuser quantum key distribution with single particles
NASA Astrophysics Data System (ADS)
Lin, Song; Wang, Hui; Guo, GongDe; Ye, GuoHua; Du, HongZhen; Liu, XiaoFen
20160301
Quantum key distribution (QKD) has been growing rapidly in recent years and becomes one of the hottest issues in quantum information science. During the implementation of QKD on a network, identity authentication has been one main problem. In this paper, an efficient authenticated multiuser quantum key distribution (MQKD) protocol with single particles is proposed. In this protocol, any two users on a quantum network can perform mutual authentication and share a secure session key with the assistance of a semihonest center. Meanwhile, the particles, which are used as quantum information carriers, are not required to be stored, therefore the proposed protocol is feasible with current technology. Finally, security analysis shows that this protocol is secure in theory.
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.
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
Hage, Ilige S; Hamade, Ramsey F
20130101
The aim of this study is to automatically discern the microfeatures in histology slides of cortical bone using pulse coupled neural networks (PCNN). To the best knowledge of the authors, utilizing PCNN in such an application has not been reported in the literature and, as such, constitutes a novel application. The network parameters are optimized using particle swarm optimization (PSO) where the PSO fitness function was introduced as the entropy and energy of the bone microconstituents extracted from a training image. Another novel contribution is combining the above with the method of adaptive threshold (T) where the PCNN algorithm is repeated until the best threshold T is found corresponding to the maximum variance between two segmented regions. To illustrate the quality of resulting segmentation according to this methodology, a comparison of the entropy/energy obtained of each pulse is reported. Suitable quality metrics (precision rate, sensitivity, specificity, accuracy, and dice) were used to benchmark the resulting segments against those found by a more traditional method namely Kmeans. The quality of the segments revealed by this methodology was found to be of much superior quality. Another testament to the quality of this methodology was that the images resulting from testing pulses were found to be of similarly good quality to those of the training images.
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.
Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah
20170101
Flexible manufacturing system (FMS) enhances the firm’s flexibility and responsiveness to the everchanging customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multiobjective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GAPSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs’ battery charge. Assessment of the numerical examples’ scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GAPSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GAPSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software. PMID:28263994
Yang, ChengHong; Lin, YuDa; Chuang, LiYeh; Chang, HsuehWei
20140101
Genegene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of genegene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing highorder interaction, and finding an available highorder model of genegene interaction remains a challenge. In this study, an improved particle swarm optimization with doublebottom chaotic maps (DBMPSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBMPSO successfully determined two to sixorder models of genegene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBMPSO can identify good models and provide higher chisquare values than conventional PSO. This study indicates that DBMPSO is a robust and precise algorithm for determination of genegene interaction models for breast cancer.
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
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.
NASA Astrophysics Data System (ADS)
Chang, YauZen; Wang, HuaiMing; Lee, ShihTseng; Wu, ChiehTsai; Hsu, MingHsi
20140201
This work investigates the calibration of a stereo vision system based on two PTZ (PanTiltZoom) cameras. As the accuracy of the system depends not only on intrinsic parameters, but also on the geometric relationships between rotation axes of the cameras, the major concern is the development of an effective and systematic way to obtain these relationships. We derived a complete geometric model of the dualPTZcamera system and proposed a calibration procedure for the intrinsic and external parameters of the model. The calibration method is based on Zhang's approach using an augmented checkerboard composed of eight small checkerboards, and is formulated as an optimization problem to be solved by an improved particle swarm optimization (PSO) method. Two Sony EVID70 PTZ cameras were used for the experiments. The rootmeansquare errors (RMSE) of corner distances in the horizontal and vertical direction are 0.192 mm and 0.115 mm, respectively. The RMSE of overlapped points between the small checkerboards is 1.3958 mm.
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
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
Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya; Tahriri, Farzad; Md Dawal, Siti Zawiah
20170101
Flexible manufacturing system (FMS) enhances the firm's flexibility and responsiveness to the everchanging customer demand by providing a fast product diversification capability. Performance of an FMS is highly dependent upon the accuracy of scheduling policy for the components of the system, such as automated guided vehicles (AGVs). An AGV as a mobile robot provides remarkable industrial capabilities for material and goods transportation within a manufacturing facility or a warehouse. Allocating AGVs to tasks, while considering the cost and time of operations, defines the AGV scheduling process. Multiobjective scheduling of AGVs, unlike single objective practices, is a complex and combinatorial process. In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GAPSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. Assessment of the numerical examples' scheduling before and after the optimization proved the applicability of all the three algorithms in decreasing the makespan and AGV numbers. The hybrid GAPSO produced the optimum result and outperformed the other two algorithms, in which the mean of AGVs operation efficiency was found to be 69.4, 74, and 79.8 percent in PSO, GA, and hybrid GAPSO, respectively. Evaluation and validation of the model was performed by simulation via Flexsim software.
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.
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.
Sheejakumari, V.; Sankara Gomathi, B.
20150101
The advantages of magnetic resonance imaging (MRI) over other diagnostic imaging modalities are its higher spatial resolution and its better discrimination of soft tissue. In the previous tissues classification method, the healthy and pathological tissues are classified from the MRI brain images using HGANN. But the method lacks sensitivity and accuracy measures. The classification method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new classification method is proposed in this paper. Here, new tissues classification method is proposed with improved particle swarm optimization (IPSO) technique to classify the healthy and pathological tissues from the given MRI images. Our proposed classification method includes the same four stages, namely, tissue segmentation, feature extraction, heuristic feature selection, and tissue classification. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of the proposed classification method in classifying the tissues and the achieved improvement in sensitivity and accuracy measures. Furthermore, the performance of the proposed technique is evaluated by comparing it with the other segmentation methods. PMID:25977706
NASA Astrophysics Data System (ADS)
Yuan, Chunhua; Wang, Jiang; Yi, Guosheng
20170301
Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.
NASA Astrophysics Data System (ADS)
Karatzas, George P.; Dokou, Zoi
20150901
Saltwater intrusion is a common phenomenon in coastal aquifers that can affect the quality of water intended for drinking and irrigation purposes. In order to provide sustainable management options for the coastal aquifer of Malia, located on the Greek island of Crete, a weighted multiobjective optimization methodology is employed. The methodology involves use of the particle swarm optimization algorithm combined with groundwater modelling. The sharpinterface approximation combined with the GhybenHerztberg equation is used to estimate the saltwaterintrusion front location. The prediction modelling results show that under the current pumping strategies (overexploitation), the saltwaterintrusion front will continue to move inland, posing a serious threat to the groundwater quality. The management goal is to maximize groundwater withdrawal rates in the existing pumping wells while inhibiting the saltwaterintrusion front at locations closer to the coastal zone. This is achieved by requiring a minimum hydraulichead value at preselected observation locations. In order to control the saltwater intrusion, a large number of pumping wells must be deactivated and alternative sources of water need to be considered.
NASA Astrophysics Data System (ADS)
Zhou, Zhiyu; Xu, Rui; Wu, Dichong; Zhu, Zefei; Wang, Haiyan
20160901
Changes in illumination will result in serious color difference evaluation errors during the dyeing process. A Baggingbased ensemble extreme learning machine (ELM) mechanism hybridized with particle swarm optimization (PSO), namely BaggingPSOELM, is proposed to develop an accurate illumination correction model for dyed fabrics. The model adopts PSO algorithm to optimize the input weights and hidden biases for the ELM neural network called PSOELM, which enhances the performance of ELM. Meanwhile, to further increase the prediction accuracy, a Bagging ensemble scheme is used to construct an independent PSOELM learning machine by taking bootstrap replicates of the training set. Then, the obtained multiple different PSOELM learners are aggregated to establish the prediction model. The proposed prediction model is evaluated with real dyed fabric images and discussed in comparison with several related methods. Experimental results show that the ensemble color constancy method is able to generate a more robust illuminant estimation model with better generalization performance.
A hybrid multiobjective particle swarm algorithm for a mixedmodel assembly line sequencing problem
NASA Astrophysics Data System (ADS)
RahimiVahed, A. R.; Mirghorbani, S. M.; Rabbani, M.
20071201
Mixedmodel assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multiobjective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Paretooptimal frontier where simultaneous minimization of the abovementioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multiobjective genetic algorithms, PSNC GA, NSGAII and SPEAII. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithms in largesized problems.
Yang, ChengHong; Chang, HsuehWei
20140101
Genegene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of genegene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing highorder interaction, and finding an available highorder model of genegene interaction remains a challenge. In this study, an improved particle swarm optimization with doublebottom chaotic maps (DBMPSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBMPSO successfully determined two to sixorder models of genegene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBMPSO can identify good models and provide higher chisquare values than conventional PSO. This study indicates that DBMPSO is a robust and precise algorithm for determination of genegene interaction models for breast cancer. PMID:24895547
NASA Astrophysics Data System (ADS)
Zhang, ZhiHua; Sheng, Zheng; Shi, HanQing
20150101
Estimating refractivity profiles from radar sea clutter is a complex nonlinear optimization problem. To deal with the illposed difficulties, an inversion algorithm, particle swarm optimization with a Lévy flight (LPSO), was proposed to be applied in the refractivity from clutter (RFC) technique to retrieve atmospheric duct in this paper. PSO has many advantages in solving continuous optimization problems, while in its late period it has slow convergence speed and low precision. Therefore, we integrate the Lévy flights into the standard PSO algorithm to improve the precision and enhance the capability of jumping out of the local optima. To verify the feasibility and validation of the LPSO for estimating atmospheric duct parameters based on the RFC method, the synthetic and Wallops98 experimental data are implemented. Numerical experiments demonstrate that the optimal solutions obtained from the hybrid algorithm are more precise and efficient. Additionally, to test the algorithm inversion performance, the antinoise ability of LPSO is analyzed. The results indicate that the LPSO algorithm has a certain antinoise ability. Finally, according to the experiment results, it can be concluded that the LPSO algorithm can provide a more precise and efficient method for nearrealtime inversion of atmospheric refractivity from radar clutter.
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.
Exactly solvable interacting twoparticle quantum graphs
NASA Astrophysics Data System (ADS)
Bolte, Jens; Garforth, George
20170301
We construct models of exactly solvable twoparticle quantum graphs with certain nonlocal twoparticle interactions, establishing appropriate boundary conditions via suitable selfadjoint realisations of the twoparticle Laplacian. Showing compatibility with the Bethe ansatz method, we calculate quantisation conditions in the form of secular equations from which the spectra can be deduced. We compare spectral statistics of some examples to well known results in random matrix theory, analysing the chaotic properties of their classical counterparts.
Quantum Walks with Neutral Atoms: Quantum Interference Effects of One and Two Particles
NASA Astrophysics Data System (ADS)
Robens, Carsten; Brakhane, Stefan; Meschede, Dieter; Alberti, A.
We report on the state of the art of quantum walk experiments with neutral atoms in statedependent optical lattices. We demonstrate a novel statedependent transport technique enabling the control of two spinselective sublattices in a fully independent fashion. This transport technique allowed us to carry out a test of singleparticle quantum interference based on the violation of the LeggettGarg inequality and, more recently, to probe twoparticle quantum interference effects with neutral atoms cooled into the motional ground state. These experiments lay the groundwork for the study of discretetime quantum walks of strongly interacting, indistinguishable particles to demonstrate quantum cellular automata of neutral atoms.
NASA Astrophysics Data System (ADS)
Wang, Qi; Zhou, Yihao; Chen, Yan Qiu
20111201
Threedimensional (3D) tracking and trajectory measurement of group translating and rotating particles may greatly help applications in collective behavior study, motion measurement, etc. Binocular stereo methods are commonly used to track and measure 3D trajectories of drifting particles. Nevertheless, binocular methods usually suffer from severe stereomatching ambiguity facing these situations even if motion constraint is adopted to disambiguate stereo matching. We try to help the disambiguating by optimizing viewpoint placement. We model the stereomatching ambiguity and test different viewpoint placements upon our geometrical analysis to show the influence on the disambiguation that utilizes motion constraint. When the targets undergo group translation and rotation which are highly ambiguous, we find the optimal viewpoint placement such that stereomatching ambiguity decreases as fast as possible over time. The optimal viewpoint placement can greatly improve the performance of existing methods.
Quantum particle probe of the Kerr naked singularity
NASA Astrophysics Data System (ADS)
Gurtug, O.; Halilsoy, M.
20170101
We investigate Kerr's timelike naked singularity within the framework of quantum mechanics. A quantum particle in the form of a massive boson is sent in the plane θ = π/2 to the naked ring singularity of Kerr which develops for the overspinning case (a>M) to test it from a quantum picture. This singularity is analysed in two different coordinate systems. We show that the spatial operator of the KleinGordon equation both in BoyerLindquist and in the dragging coordinate systems has a unique selfadjoint extension. As a result, the classical Kerr's ring singularity becomes quantum regular, if it is probed with massive bosonic particles obeying the KleinGordon equation.
Performances and robustness of quantum teleportation with identical particles
Marzolino, Ugo; Buchleitner, Andreas
20160101
When quantum teleportation is performed with truly identical massive particles, indistinguishability allows us to teleport addressable degrees of freedom which do not identify particles, but, for example, orthogonal modes. The key resource of the protocol is a state of entangled modes, but the conservation of the total number of particles does not allow for perfect deterministic teleportation unless the number of particles in the resource state goes to infinity. Here, we study the convergence of teleportation performances in the above limit and provide sufficient conditions for asymptotic perfect teleportation. We also apply these conditions to the case of resource states affected by noise. PMID:26997896
Performances and robustness of quantum teleportation with identical particles.
Marzolino, Ugo; Buchleitner, Andreas
20160101
When quantum teleportation is performed with truly identical massive particles, indistinguishability allows us to teleport addressable degrees of freedom which do not identify particles, but, for example, orthogonal modes. The key resource of the protocol is a state of entangled modes, but the conservation of the total number of particles does not allow for perfect deterministic teleportation unless the number of particles in the resource state goes to infinity. Here, we study the convergence of teleportation performances in the above limit and provide sufficient conditions for asymptotic perfect teleportation. We also apply these conditions to the case of resource states affected by noise.
Quantum Gravity Explanation of the WaveParticle Duality
NASA Astrophysics Data System (ADS)
Winterberg, Friedwardt
20160301
A quantum gravity explanation of the quantummechanical waveparticle duality is given by the wattless emission of gravitational waves from a particle described by the Dirac equation. This explanation is possible through the existence of negative energy, and hence negative mass solutions of Einstein's gravitational field equations. They permit to understand the Dirac equation as the equation for a gravitationally bound positivenegative mass (poledipole particle) twobody configuration, with the mass of the Dirac particle equal to the positive mass of the gravitational field binding the positive with the negative mass particle, and with the positive and negative mass particles making a luminal ``Zitterbewegung'' (quivering motion), emitting a wattless oscillating positivenegative space curvature wave. Is it shown that this thusly produced ``Zitterbewegung'' reproduces the quantum potential of the Madelungtransformed Schrödinger equation. The wattless gravitational wave emitted by the quivering particles is conjectured to be the de Broglie pilot wave.
Zyout, Imad; Czajkowska, Joanna; Grzegorzek, Marcin
20151201
The high number of false positives and the resulting number of avoidable breast biopsies are the major problems faced by current mammography Computer Aided Detection (CAD) systems. False positive reduction is not only a requirement for mass but also for calcification CAD systems which are currently deployed for clinical use. This paper tackles two problems related to reducing the number of false positives in the detection of all lesions and masses, respectively. Firstly, textural patterns of breast tissue have been analyzed using several multiscale textural descriptors based on wavelet and gray level cooccurrence matrix. The second problem addressed in this paper is the parameter selection and performance optimization. For this, we adopt a model selection procedure based on Particle Swarm Optimization (PSO) for selecting the most discriminative textural features and for strengthening the generalization capacity of the supervised learning stage based on a Support Vector Machine (SVM) classifier. For evaluating the proposed methods, two sets of suspicious mammogram regions have been used. The first one, obtained from Digital Database for Screening Mammography (DDSM), contains 1494 regions (1000 normal and 494 abnormal samples). The second set of suspicious regions was obtained from database of Mammographic Image Analysis Society (miniMIAS) and contains 315 (207 normal and 108 abnormal) samples. Results from both datasets demonstrate the efficiency of using PSO based model selection for optimizing both classifier hyperparameters and parameters, respectively. Furthermore, the obtained results indicate the promising performance of the proposed textural features and more specifically, those based on cooccurrence matrix of wavelet image representation technique.
Ma, Denglong; Tan, Wei; Zhang, Zaoxiao; Hu, Jun
20170305
In order to identify the parameters of hazardous gas emission source in atmosphere with less previous information and reliable probability estimation, a hybrid algorithm coupling Tikhonov regularization with particle swarm optimization (PSO) was proposed. When the source location is known, the source strength can be estimated successfully by common Tikhonov regularization method, but it is invalid when the information about both source strength and location is absent. Therefore, a hybrid method combining linear Tikhonov regularization and PSO algorithm was designed. With this method, the nonlinear inverse dispersion model was transformed to a linear form under some assumptions, and the source parameters including source strength and location were identified simultaneously by linear TikhonovPSO regularization method. The regularization parameters were selected by Lcurve method. The estimation results with different regularization matrixes showed that the confidence interval with highorder regularization matrix is narrower than that with zeroorder regularization matrix. But the estimation results of different source parameters are close to each other with different regularization matrixes. A nonlinear TikhonovPSO hybrid regularization was also designed with primary nonlinear dispersion model to estimate the source parameters. The comparison results of simulation and experiment case showed that the linear TikhonovPSO method with transformed linear inverse model has higher computation efficiency than nonlinear TikhonovPSO method. The confidence intervals from linear TikhonovPSO are more reasonable than that from nonlinear method. The estimation results from linear TikhonovPSO method are similar to that from single PSO algorithm, and a reasonable confidence interval with some probability levels can be additionally given by TikhonovPSO method. Therefore, the presented linear TikhonovPSO regularization method is a good potential method for hazardous emission
NASA Astrophysics Data System (ADS)
Khehra, Baljit Singh; Pharwaha, Amar Partap Singh
20160601
Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NPhard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeographybased optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSObased and BBObased algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GAbased algorithm.
NASA Astrophysics Data System (ADS)
Zhang, ChangJiang; Dai, LiJie; Ma, LeiMing
20161001
The data of current PM2.5 model forecasting greatly deviate from the measured concentration. In order to solve this problem, Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) are combined to build a rolling forecasting model. The important parameters (C and γ) of SVM are optimized by PSO. The data (from February to July in 2015), consisting of measured PM2.5 concentration, PM2.5 model forecasting concentration and five main model forecasting meteorological factors, are provided by Shanghai Meteorological Bureau in Pudong New Area. The rolling model is used to forecast hourly PM2.5 concentration in 12 hours in advance and the nighttime average concentration (mean value from 9 pm to next day 8 am) during the upcoming day. The training data and the optimal parameters of SVM model are different in every forecasting, that is to say, different models (dynamic models) are built in every forecasting. SVM model is compared with Radical Basis Function Neural Network (RBFNN), Multivariable Linear Regression (MLR) and WRFCHEM. Experimental results show that the proposed model improves the forecasting accuracy of hourly PM2.5 concentration in 12 hours in advance and nighttime average concentration during the upcoming day. SVM model performs better than MLR, RBFNN and WRFCHEM. SVM model greatly improves the forecasting accuracy of PM2.5 concentration one hour in advance, according with the result concluded from previous research. The rolling forecasting model can be applied to the field of PM2.5 concentration forecasting, and can offer help to meteorological administration in PM2.5 concentration monitoring and forecasting.
Modiri, A; Gu, X; Sawant, A
20140615
Purpose: We present a particle swarm optimization (PSO)based 4D IMRT planning technique designed for dynamic MLC tracking delivery to lung tumors. The key idea is to utilize the temporal dimension as an additional degree of freedom rather than a constraint in order to achieve improved sparing of organs at risk (OARs). Methods: The target and normal structures were manually contoured on each of the ten phases of a 4DCT scan acquired from a lung SBRT patient who exhibited 1.5cm tumor motion despite the use of abdominal compression. Corresponding ten IMRT plans were generated using the Eclipse treatment planning system. These plans served as initial guess solutions for the PSO algorithm. Fluence weights were optimized over the entire solution space i.e., 10 phases × 12 beams × 166 control points. The size of the solution space motivated our choice of PSO, which is a highly parallelizable stochastic global optimization technique that is wellsuited for such large problems. A summed fluence map was created using an inhouse Bspline deformable image registration. Each plan was compared with a corresponding, internal target volume (ITV)based IMRT plan. Results: The PSO 4D IMRT plan yielded comparable PTV coverage and significantly higher dose—sparing for parallel and serial OARs compared to the ITVbased plan. The dosesparing achieved via PSO4DIMRT was: lung Dmean = 28%; lung V20 = 90%; spinal cord Dmax = 23%; esophagus Dmax = 31%; heart Dmax = 51%; heart Dmean = 64%. Conclusion: Truly 4D IMRT that uses the temporal dimension as an additional degree of freedom can achieve significant dose sparing of serial and parallel OARs. Given the large solution space, PSO represents an attractive, parallelizable tool to achieve globally optimal solutions for such problems. This work was supported through funding from the National Institutes of Health and Varian Medical Systems. Amit Sawant has research funding from Varian Medical Systems, VisionRT Ltd. and Elekta.
Ling, QingHua; Song, YuQing; Han, Fei; Yang, Dan; Huang, DeShuang
20160101
For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct inputtooutput links is one of suitable baseclassifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm leastsquare method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.
NASA Astrophysics Data System (ADS)
Yadav, Basant; Ch, Sudheer; Mathur, Shashi; Adamowski, Jan
20161201
Insitu bioremediation is the most common groundwater remediation procedure used for treating organically contaminated sites. A simulationoptimization approach, which incorporates a simulation model for groundwaterflow and transport processes within an optimization program, could help engineers in designing a remediation system that best satisfies management objectives as well as regulatory constraints. Insitu bioremediation is a highly complex, nonlinear process and the modelling of such a complex system requires significant computational exertion. Soft computing techniques have a flexible mathematical structure which can generalize complex nonlinear processes. In insitu bioremediation management, a physicallybased model is used for the simulation and the simulated data is utilized by the optimization model to optimize the remediation cost. The recalling of simulator to satisfy the constraints is an extremely tedious and time consuming process and thus there is need for a simulator which can reduce the computational burden. This study presents a simulationoptimization approach to achieve an accurate and cost effective insitu bioremediation system design for groundwater contaminated with BTEX (Benzene, Toluene, Ethylbenzene, and Xylenes) compounds. In this study, the Extreme Learning Machine (ELM) is used as a proxy simulator to replace BIOPLUME III for the simulation. The selection of ELM is done by a comparative analysis with Artificial Neural Network (ANN) and Support Vector Machine (SVM) as they were successfully used in previous studies of insitu bioremediation system design. Further, a singleobjective optimization problem is solved by a coupled Extreme Learning Machine (ELM)Particle Swarm Optimization (PSO) technique to achieve the minimum cost for the insitu bioremediation system design. The results indicate that ELM is a faster and more accurate proxy simulator than ANN and SVM. The total cost obtained by the ELMPSO approach is held to a minimum
NASA Astrophysics Data System (ADS)
Mansour, F. A.; Nizam, M.; Anwar, M.
20170201
This research aims to predict the optimum surface orientation angles in solar panel installation to achieve maximum solar radiation. Incident solar radiation is calculated using koronakis mathematical model. Particle Swarm Optimization (PSO) is used as computational method to find optimum angle orientation for solar panel installation in order to get maximum solar radiation. A series of simulation has been carried out to calculate solar radiation based on monthly, seasonally, semiyearly and yearly period. Southfacing was calculated also as comparison of proposed method. Southfacing considers azimuth of 0°. Proposed method attains higher incident predictions than Southfacing that recorded 2511.03 kWh/m2for monthly. It were about 2486.49 kWh/m2, 2482.13 kWh/m2and 2367.68 kWh/m2 for seasonally, semiyearly and yearly. Southfacing predicted approximately 2496.89 kWh/m2, 2472.40 kWh/m2, 2468.96 kWh/m2, 2356.09 kWh/m2for monthly, seasonally, semiyearly and yearly periods respectively. Semiyearly is the best choice because it needs twice adjustments of solar panel in a year. Yet it considers inefficient to adjust solar panel position in every season or monthly with no significant solar radiation increase than semiyearly and solar tracking device still considers costly in solar energy system. PSO was able to predict accurately with simple concept, easy and computationally efficient. It has been proven by finding the best fitness faster.
Ling, QingHua; Song, YuQing; Han, Fei; Yang, Dan; Huang, DeShuang
20160101
For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct inputtooutput links is one of suitable baseclassifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm leastsquare method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system. PMID:27835638
Quantum spectra of Raman photon pairs from a mesoscopic particle
NASA Astrophysics Data System (ADS)
Ooi, C. H. Raymond; Loh, W. M. Edmund; Kam, C. H.
20150601
Quantum Langevin formalism with noise operators is used to provide quantum descriptions of photon pairs (the Stokes and antiStokes fields) emitted by a mesoscopic spherical particle composed of quantum particles in a double Raman configuration. The spectra of the fields obtained are sensitive to the dimension of the microsphere and can be controlled by pump and control laser fields. Spectral peaks due to quantum coherence are Stark shifted by the laser fields experiencing autofocusing inside the spherical particle, causing broadening of peaks as the size of the microsphere increases. The antinormalorder spectrum is found to be identical to the normalorder spectrum. The antiStokes spectrum is identical to the Stokes spectrum when the linear dispersion is neglected. Frequencydependent dielectric functions of the Stokes and antiStokes spectra corresponding to the linear dispersions of the particle yield narrow morphologydependent resonance gain peaks at certain frequencies of the Stokes and antiStokes spectra that depend not only on the particle size but also on the angle of observation.
Quantum principles and free particles. [evaluation of partitions
NASA Technical Reports Server (NTRS)
19760101
The quantum principles that establish the energy levels and degeneracies needed to evaluate the partition functions are explored. The uncertainty principle is associated with the dual waveparticle nature of the model used to describe quantized gas particles. The Schroedinger wave equation is presented as a generalization of Maxwell's wave equation; the former applies to all particles while the Maxwell equation applies to the special case of photon particles. The size of the quantum cell in phase space and the representation of momentum as a space derivative operator follow from the uncertainty principle. A consequence of this is that steadystate problems that are spacetime dependent for the classical model become only space dependent for the quantum model and are often easier to solve. The partition function is derived for quantized free particles and, at normal conditions, the result is the same as that given by the classical phase integral. The quantum corrections that occur at very low temperatures or high densities are derived. These corrections for the EinsteinBose gas qualitatively describe the condensation effects that occur in liquid helium, but are unimportant for most practical purposes otherwise. However, the corrections for the FermiDirac gas are important because they quantitatively describe the behavior of highdensity conduction electron gases in metals and explain the zero point energy and low specific heat exhibited in this case.
An extended relativistic quantum oscillator for ? particles
NASA Astrophysics Data System (ADS)
Nedjadi, Y.; AitTahar, S.; Barrett, R. C.
19980401
We introduce the extended DuffinKemmerPetiau (DKP) oscillator obtained by combining two relativistic quantum oscillator models. In a study analogous to Kukulin, Loyola and Moshinsky's work on extended Dirac oscillators, we investigate whether this extended version has oscillator shells controllably independent from the spinorbit coupling. This extended DKP oscillator is found to be exactly solvable for natural parity states. We calculate and discuss both the natural and unnaturalparity eigenspectra of its spin1 representation.
Single particle sources and quantum heat fluctuations
NASA Astrophysics Data System (ADS)
Battista, F.
20141001
The miniaturisation of electronic devices has been a wellknown trend in engineering over almost 50 years. The technological advancement in the field can now provide an astonishing control of charge transport in mesoscopic structures. Single particle pumping, namely the control in time and space of the flow of an arbitrarily small number of electrons or holes, has been realised in various kind of structure with, in some cases, very high accuracies. The first half of the manuscript provides a brief overview of different experimental realisations of single particle sources. Though these devices allow to minimise charge fluctuations in the charge current, because of Heisenberg's uncertainty principle, the emitted particles are characterised by energy fluctuations. The consequences of it are of great relevance and presented in the second part of the paper.
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
Single particle density of trapped interacting quantum gases Bala, Renu; Bosse, J.; Pathak, K. N. 20150515 An expression for single particle density for trapped interacting gases has been obtained in first order of interaction using Green’s function method. Results are easily simplified for homogeneous quantum gases and are found to agree with famous results obtained by HuangYangLuttinger and LeeYang.
