Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary computing based mobile robot localization
Engineering Applications of Artificial Intelligence
A particle swarm optimization approach to nonlinear rational filter modeling
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Swarm optimized organizing map (SWOM): A swarm intelligence basedoptimization of self-organizing map
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Learning to play games using a PSO-based competitive learning approach
IEEE Transactions on Evolutionary Computation
An approach to multimodal biomedical image registration utilizing particle swarm optimization
IEEE Transactions on Evolutionary Computation
Stability analysis of the particle dynamics in particle swarm optimizer
IEEE Transactions on Evolutionary Computation
A Modified PSO Structure Resulting in High Exploration Ability With Convergence Guaranteed
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Parallel particle swarm optimization (PPSO) on the coverage problem in pursuit-evasion games
Proceedings of the 2012 Symposium on High Performance Computing
Digital Signal Processing
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Particle Swarm Optimization (PSO) is an algorithm motivated by biological systems. However, PSO implementations are sequential, meaning that particles cannot simultaneously interact with other members in the same swarm. This study tries to develop an exact PSO model whose particles simultaneously interact with each other. To model limited communication capability, particles in a swarm are separated into several subgroups. Communication among the subgroups is implemented by parallel computation models based on broadcast, star, migration and diffusion network topologies. Due to the expense and difficulty of true parallel computation, multiple threads are used to model simultaneous particle interaction. We compare the four parallel PSO models and the traditional sequential computation model using measures of convergence error, generations to convergence and execution time. Three experiments to examine the performance of the parallel PSO models are also included.