Particle swarm optimization

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In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. (Wikipedia.org)






Conferences related to Particle swarm optimization

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2014 IEEE Congress on Evolutionary Computation (CEC)

IEEE Congress on Evolutionary Computation is the largest technical event in the field of evolutionary computation. In 2014, International Joint Conference on Neural Networks will be part of the 2104 IEEE World Congress on Computational Intelligence.

  • 2013 IEEE Congress on Evolutionary Computation (CEC)

    CEC 2013 will bring together researchers and practitioners in the field of evolutionary computation and computational intelligence from around the globe. Theory, applications, algorithmic developments and all other aspects of evolutionary computation and related areas (i.e., any other bio-inspired metaheuristics) are welcome to contribute to this conference.

  • 2012 IEEE Congress on Evolutionary Computation (CEC)

    The annual IEEE CEC is one of the leading events in the field of evolutionary computation.

  • 2011 IEEE Congress on Evolutionary Computation (CEC)

    Annual Congress on Evolutionary Computation.


2014 IEEE World Congress on Computational Intelligence (WCCI)

IEEE World Congress on Computational Intelligence (IEEE WCCI) is the largest technical event in the field of computational intelligence. IEEE WCCI 2014 will host three conferences: The 2014 International Joint Conference on Neural Networks, the 2014 IEEE International Conference on Fuzzy Systems, and the 2014 IEEE Congress on Evolutionary Computation.


2013 IEEE Symposium on Swarm Intelligence (SIS)

It will be part of the IEEE Symposium Series on Computational Intelligence 2013, which consists of over 20 symposia. The theme of the SIS2013 is to provide a platform for researchers, academicians, students, engineers, and government officers from all over the world to share and exchange information in the swarm intelligence research areas ranging from algorithm development to real-world applications. Authors are invited to submit their original and unpublished work related to swarm intelligence, including research, theory, development, and applications.


2012 24th Chinese Control and Decision Conference (CCDC)

Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.


2012 5th International Conference on Intelligent Networks and Intelligent Systems (ICINIS)

The 5th International Conference on Intelligent Networks and Intelligent Systems (ICINIS 2012) aims to provide an international forum for scientists, engineers, and educators to present the state of the art of intelligent networks and systems research and applications in various disciplines.

  • 2011 4th International Conference on Intelligent Networks and Intelligent Systems (ICINIS)

    The 4th International Conference on Intelligent Networks and Intelligent Systems (ICINIS 2011) aims to provide a high-level international forum for scientists, engineers, and educators to present the state of the art of intelligent networks and systems research and applications in various disciplines.

  • 2010 3rd International Conference on Intelligent Networks and Intelligent Systems (ICINIS)

    The Field of Interest of the Society shall be the theory, design, application, and develo pment of intelligent computer networks an d systems using biologically inspired co mputational paradigms such as neural net works, evolutionary algorithms and fuzzy sets.

  • 2009 Second International Conference on Intelligent Networks and Intelligent Systems (ICINIS)

    The INASS, a non-profit organization, is the professional association for the advancement of intelligent technology. The INASS devotes to promote the engineering process of creating, developing, integrating, sharing, and applying knowledge about intelligent science and information technologies for the benefit of humanity and the profession . About 30% of the accepted papers of this conference will be selected for possible publication in the special issue of the International Journal of Intelligent Engineeri


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Periodicals related to Particle swarm optimization

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Evolutionary Computation, IEEE Transactions on

Papers on application, design, and theory of evolutionary computation, with emphasis given to engineering systems and scientific applications. Evolutionary optimization, machine learning, intelligent systems design, image processing and machine vision, pattern recognition, evolutionary neurocomputing, evolutionary fuzzy systems, applications in biomedicine and biochemistry, robotics and control, mathematical modelling, civil, chemical, aeronautical, and industrial engineering applications.


Systems, Man and Cybernetics, Part A, IEEE Transactions on

Systems engineering, including efforts that involve issue formnaulations, issue analysis and modeling, and decision making and issue interpretation at any of the life-cycle phases associated with the definition, development, and implementation of large systems. It will also include efforts that relate to systems management, systems engineering processes and a variety of systems engineering methods such as optimization, modeling and simulation. ...


Systems, Man, and Cybernetics, Part B, IEEE Transactions on

The scope of the IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or between machines, humans, and organizations. The scope of Part B includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, ...


Transactions on Sustainable Energy

The IEEE Transactions on Sustainable Energy is a cross disciplinary and internationally archival journal aimed at disseminating results of research on sustainable energy that relates to, arises from, or deliberately influences energy generation, transmission, distribution and delivery. The journal will publish original research on theories and development on principles of sustainable energy technologies and systems.This journal will cover the following ...



Most published Xplore authors for Particle swarm optimization

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Xplore Articles related to Particle swarm optimization

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A Comparison of methods for leader selection in many-objective problems

Olacir Rodrigues Castro Junior; Andre Britto; Aurora Pozo 2012 IEEE Congress on Evolutionary Computation, 2012

A well-known problem faced by Multi-Objective Particle Swarm Optimization Algorithms (MOPSO) is the deterioration of its search ability when the number of objectives scales up. In the literature some techniques were proposed to overcome these limitations, however, most of them focuses on alternatives to the non-domination relation. In this work, a different direction is explored, and some specific aspects of ...


Dynamic Multi-Swarm Particle Swarm Optimization for Multi-objective optimization problems

J. J. Liang; B. Y. Qu; P. N. Suganthan; B. Niu 2012 IEEE Congress on Evolutionary Computation, 2012

In this paper, Dynamic Multi-Swarm Particle Swarm Optimizer (DMS-PSO) which was first designed for solving single objective optimizations problems is extended to solve Multi-objective optimization problems with constraints. Through analysis, novel pbest and lbest updating criteria which are more suitable for solving Multi-objective optimization problems are proposed. By combining the external archive and the novel updating criteria, excellent performance is ...


A family Particle Swarm Optimization based on the family tree

Zhenzhou An; Xinling Shi; Junhua Zhang; Baolei Li; Aimin Miao 2011 International Conference on Image Analysis and Signal Processing, 2011

The concept of the family was previously introduced into Particle Swarm Optimization (PSO). To further study the multi-group structure of the Family PSO (FPSO), this paper introduces the family tree into the FPSO. It made different families form a family tree and a swarm consisted of some family trees. In the experiment, topological distance was used to form a family ...


On the influence of the swimming operators in the Fish School Search algorithm

C. J. A. Bastos Filho; F. B. Lima Neto; M. F. C. Sousa; M. R. Pontes; S. S. Madeiro Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on, 2009

Real world engineer problems sometimes involve high dimensional spaces. It makes them hard to compute. A common approach to tackle such challenges is to apply swarm based or evolutionary algorithms. Fish School Search (FSS) is one of such techniques that excels on difficult search problems. As FSS is a recent technique and only output results were investigated so far. This ...


Congestion control in computer networks: Application of piece-wise affine controller and particle swarm optimization

Sł awomir Grzyb; Przemysł aw Orł owski Methods and Models in Automation and Robotics (MMAR), 2014 19th International Conference On, 2014

Congestion control plays a significant role in maintaining sufficient network throughput. Variety of methods and algorithms are proposed to solve the bottleneck issue. This paper describes a method based on a particle swarm optimization algorithm and piecewise affine controller for non-stationary, discrete, dynamical model of data exchange network. This solution allows active network nodes buffer utilization to be adapted to ...


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Educational Resources on Particle swarm optimization

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eLearning

A Comparison of methods for leader selection in many-objective problems

Olacir Rodrigues Castro Junior; Andre Britto; Aurora Pozo 2012 IEEE Congress on Evolutionary Computation, 2012

A well-known problem faced by Multi-Objective Particle Swarm Optimization Algorithms (MOPSO) is the deterioration of its search ability when the number of objectives scales up. In the literature some techniques were proposed to overcome these limitations, however, most of them focuses on alternatives to the non-domination relation. In this work, a different direction is explored, and some specific aspects of ...


Dynamic Multi-Swarm Particle Swarm Optimization for Multi-objective optimization problems

J. J. Liang; B. Y. Qu; P. N. Suganthan; B. Niu 2012 IEEE Congress on Evolutionary Computation, 2012

In this paper, Dynamic Multi-Swarm Particle Swarm Optimizer (DMS-PSO) which was first designed for solving single objective optimizations problems is extended to solve Multi-objective optimization problems with constraints. Through analysis, novel pbest and lbest updating criteria which are more suitable for solving Multi-objective optimization problems are proposed. By combining the external archive and the novel updating criteria, excellent performance is ...


A family Particle Swarm Optimization based on the family tree

Zhenzhou An; Xinling Shi; Junhua Zhang; Baolei Li; Aimin Miao 2011 International Conference on Image Analysis and Signal Processing, 2011

The concept of the family was previously introduced into Particle Swarm Optimization (PSO). To further study the multi-group structure of the Family PSO (FPSO), this paper introduces the family tree into the FPSO. It made different families form a family tree and a swarm consisted of some family trees. In the experiment, topological distance was used to form a family ...


On the influence of the swimming operators in the Fish School Search algorithm

C. J. A. Bastos Filho; F. B. Lima Neto; M. F. C. Sousa; M. R. Pontes; S. S. Madeiro Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on, 2009

Real world engineer problems sometimes involve high dimensional spaces. It makes them hard to compute. A common approach to tackle such challenges is to apply swarm based or evolutionary algorithms. Fish School Search (FSS) is one of such techniques that excels on difficult search problems. As FSS is a recent technique and only output results were investigated so far. This ...


Congestion control in computer networks: Application of piece-wise affine controller and particle swarm optimization

Sł awomir Grzyb; Przemysł aw Orł owski Methods and Models in Automation and Robotics (MMAR), 2014 19th International Conference On, 2014

Congestion control plays a significant role in maintaining sufficient network throughput. Variety of methods and algorithms are proposed to solve the bottleneck issue. This paper describes a method based on a particle swarm optimization algorithm and piecewise affine controller for non-stationary, discrete, dynamical model of data exchange network. This solution allows active network nodes buffer utilization to be adapted to ...


More eLearning Resources

IEEE-USA E-Books

  • Intelligence-Based Hybrid Integration

    In this chapter, intelligence-based design and control integration will be proposed for the manufacturing system that has a hybrid discrete/continuous dynamics. The fuzzy modeling method is first used to approximate the nonlinear process in a large operating region, upon which fuzzy control rules are developed to stabilize the process with the desired robust tracking performance. Then, the steady-state economic design and the controller design are integrated into a unified optimization, which will be solved by a particle swarm optimization (PSO)-based hierarchical optimization method. This intelligent solution can achieve the desired economic performance as well as the satisfactory dynamic performance. The proposed method is compared with the traditional sequential design method and a traditional integration method on controlling the temperature profile of a curing process in integrated circuits (IC) packaging industry.

  • Hybrid Systems

    This chapter contains sections titled: Introduction Capacitor Sizing and Location and Analytical Sensitivities Unit Commitment, Fuzzy Sets, and Cleverer Chromosomes Voltage/Var Control and Loss Reduction in Distribution Networks with an Evolutionary Self-Adaptive Particle Swarm Optimization Algorithm: EPSO Conclusions References

  • Optimal Power Flow

    This chapter contains sections titled: Introduction Newton Method Gradient Method Linear Programming OPF Modified Interior Point OPF OPF with Phase Shifter Multiple-Objectives OPF Particle Swarm Optimization for OPF References

  • No title

    This work aims to provide new introduction to the particle swarm optimization methods using a formal analogy with physical systems. By postulating that the swarm motion behaves similar to both classical and quantum particles, we establish a direct connection between what are usually assumed to be separate fields of study, optimization and physics. Within this framework, it becomes quite natural to derive the recently introduced quantum PSO algorithm from the Hamiltonian or the Lagrangian of the dynamical system. The physical theory of the PSO is used to suggest some improvements in the algorithm itself, like temperature acceleration techniques and the periodic boundary condition. At the end, we provide a panorama of applications demonstrating the power of the PSO, classical and quantum, in handling difficult engineering problems. The goal of this work is to provide a general multi-disciplinary view on various topics in physics, mathematics, and engineering by illustrating their interd pendence within the unified framework of the swarm dynamics. Table of Contents: Introduction / The Classical Particle Swarm Optimization Method / Boundary Conditions for the PSO Method / The Quantum Particle Swarm Optimization / Bibliography /Index

  • Fundamentals of Particle Swarm Optimization Techniques

    This chapter contains sections titled: Introduction Basic Particle Swarm Optimization Variations of Particle Swarm Optimization Research Areas and Applications Conclusions References

  • Reactive Power Optimization

    This chapter contains sections titled: Introduction Classic Method for Reactive Power Dispatch Linear Programming Method of VAR Optimization Interior Point Method for VAR Optimization Problem NLONN Approach VAR Optimization by Evolutionary Algorithm VAR Optimization by Particle Swarm Optimization Algorithm Reactive Power Pricing Calculation References

  • GA Extensions

    This chapter contains sections titled: Selecting Population Size and Mutation Rate Particle Swarm Optimization (PSO) Multiple-Objective Optimization

  • Unit Commitment

    This chapter contains sections titled: Introduction Priority Method Dynamic Programming Method Lagrange Relaxation Method Evolutionary Programming-Based Tabu Search Method Particle Swarm Optimization for Unit Commitment Analytic Hierarchy Process References

  • No title

    Video context analysis is an active and vibrant research area, which provides means for extracting, analyzing and understanding behavior of a single target and multiple targets. Over the last few decades, computer vision researchers have been working to improve the accuracy and robustness of algorithms to analyse the context of a video automatically. In general, the research work in this area can be categorized into three major topics: 1) counting number of people in the scene 2) tracking individuals in a crowd and 3) understanding behavior of a single target or multiple targets in the scene. This book focusses on tracking individual targets and detecting abnormal behavior of a crowd in a complex scene. Firstly, this book surveys the state-of-the-art methods for tracking multiple targets in a complex scene and describes the authors' approach for tracking multiple targets. The proposed approach is to formulate the problem of multi-target tracking as an optimization problem of finding ynamic optima (pedestrians) where these optima interact frequently. A novel particle swarm optimization (PSO) algorithm that uses a set of multiple swarms is presented. Through particles and swarms diversification, motion prediction is introduced into the standard PSO, constraining swarm members to the most likely region in the search space. The social interaction among swarm and the output from pedestrians-detector are also incorporated into the velocity-updating equation. This allows the proposed approach to track multiple targets in a crowded scene with severe occlusion and heavy interactions among targets. The second part of this book discusses the problem of detecting and localising abnormal activities in crowded scenes. We present a spatio-temporal Laplacian Eigenmap method for extracting different crowd activities from videos. This method learns the spatial and temporal variations of local motions in an embedded space and employs representatives of different activities to const uct the model which characterises the regular behavior of a crowd. This model of regular crowd behavior allows for the detection of abnormal crowd activities both in local and global context and the localization of regions which show abnormal behavior.

  • Optimal Power Flow

    This chapter selects several classic optimal power flow (OPF) algorithms and describes their implementation details. These algorithms include traditional methods such as Newton method, gradient method, linear programming, as well as the latest methods such as modified interior point (IP) method, analytic hierarchy process (AHP), and particle swarm optimization (PSO) method. The goal of OPF is to find the optimal settings of a given power system network that optimizes the system objective functions such as total generation cost, system loss, bus voltage deviation, emission of generating units, number of control actions, and load shedding while satisfying its power flow equations, system security, and equipment operating limits. The phase shifters are adjusted sequentially and their direction of adjustments are governed by the impact on the primary objective function of minimal line overload, in the search technique. This chapter focuses on applying PSO methods to solve the OPF problem.



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