Издательство InTech, 2009, -486 pp.
Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling.
PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles.
This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field.
This book is certainly a small sample of the research activity on Particle Swarm Optimization going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects.
Novel Binary Particle Swarm Optimization.
Swarm Intelligence Applications in Electric Machines.
Particle Swarm Optimization for HW/SW Partitioning.
Particle Swarms in Statistical Physics.
Individual Parameter Selection Strategy for Particle Swarm Optimization.
Personal Best Oriented Particle Swarm Optimizer.
Particle Swarm Optimization for Power Dispatch with Pumped Hydro.
Searching for the best Points of interpolation using swarm intelligence techniques.
Particle Swarm Optimization and Other Metaheuristic Methods in Hybrid Flow Shop Scheduling Problem.
A Particle Swarm Optimization technique used for the improvement of analogue circuit performances.
Particle Swarm Optimization Applied for Locating an Intruder by an Ultra-Wideband Radar Network.
Application of Particle Swarm Optimization in Accurate Segmentation of Brain MR Images.
Swarm Intelligence in Portfolio Selection.
Enhanced Particle Swarm Optimization for Design and Optimization of Frequency Selective Surfaces and Artificial Magnetic Conductors.
Search Performance Improvement for PSO in High Dimensional Sapece.
Finding Base-Station Locations in Two-Tiered Wireless Sensor Networks by Particle Swarm Optimization.
Particle Swarm Optimization Algorithm for Transportation Problems 2.
A Particle Swarm Optimisation Approach to Graph Permutations.
Particle Swarm Optimization Applied to Parameters Leaing of Probabilistic Neural Networks for Classification of Economic Activities.
Path Planning for Formations of Mobile Robots using PSO Technique.
Simultaneous Perturbation Particle Swarm Optimization and Its FPGA Implementation.
Particle Swarm Optimization with Exteal Archives for Interactive Fuzzy Multiobjective Nonlinear Programming.
Using Opposition-based Leaing with Particle Swarm Optimization and Barebones Differential Evolution.
Particle Swarm Optimization: Dynamical Analysis through Fractional Calculus.
Discrete Particle Swarm Optimization Algorithm for Flowshop Scheduling.
A Radial Basis Function Neural Network with Adaptive Structure via Particle Swarm Optimization.
A Novel Binary Coding Particle Swarm Optimization for Feeder Reconfiguration.
Particle Swarms for Continuous, Binary, and Discrete Search Spaces.
Application of Particle Swarm Optimization Algorithm in Smart Antenna Array Systems.
Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling.
PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles.
This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field.
This book is certainly a small sample of the research activity on Particle Swarm Optimization going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects.
Novel Binary Particle Swarm Optimization.
Swarm Intelligence Applications in Electric Machines.
Particle Swarm Optimization for HW/SW Partitioning.
Particle Swarms in Statistical Physics.
Individual Parameter Selection Strategy for Particle Swarm Optimization.
Personal Best Oriented Particle Swarm Optimizer.
Particle Swarm Optimization for Power Dispatch with Pumped Hydro.
Searching for the best Points of interpolation using swarm intelligence techniques.
Particle Swarm Optimization and Other Metaheuristic Methods in Hybrid Flow Shop Scheduling Problem.
A Particle Swarm Optimization technique used for the improvement of analogue circuit performances.
Particle Swarm Optimization Applied for Locating an Intruder by an Ultra-Wideband Radar Network.
Application of Particle Swarm Optimization in Accurate Segmentation of Brain MR Images.
Swarm Intelligence in Portfolio Selection.
Enhanced Particle Swarm Optimization for Design and Optimization of Frequency Selective Surfaces and Artificial Magnetic Conductors.
Search Performance Improvement for PSO in High Dimensional Sapece.
Finding Base-Station Locations in Two-Tiered Wireless Sensor Networks by Particle Swarm Optimization.
Particle Swarm Optimization Algorithm for Transportation Problems 2.
A Particle Swarm Optimisation Approach to Graph Permutations.
Particle Swarm Optimization Applied to Parameters Leaing of Probabilistic Neural Networks for Classification of Economic Activities.
Path Planning for Formations of Mobile Robots using PSO Technique.
Simultaneous Perturbation Particle Swarm Optimization and Its FPGA Implementation.
Particle Swarm Optimization with Exteal Archives for Interactive Fuzzy Multiobjective Nonlinear Programming.
Using Opposition-based Leaing with Particle Swarm Optimization and Barebones Differential Evolution.
Particle Swarm Optimization: Dynamical Analysis through Fractional Calculus.
Discrete Particle Swarm Optimization Algorithm for Flowshop Scheduling.
A Radial Basis Function Neural Network with Adaptive Structure via Particle Swarm Optimization.
A Novel Binary Coding Particle Swarm Optimization for Feeder Reconfiguration.
Particle Swarms for Continuous, Binary, and Discrete Search Spaces.
Application of Particle Swarm Optimization Algorithm in Smart Antenna Array Systems.