Parallel Particle Swarm Optimization
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PSO is a population based evolutionary algorithm and is motivated from the simulation of social behavior, which differs from the natural selection scheme of genetic algorithms. It is an optimization technique based on swarm intelligence, which simulates the bio-inspired behavior. PSO is a popular global search method and the algorithm is being widely used in conjunction with several other algorithms in different fields of study. Modern day computational problems demand highly capable processing machines and improved optimization techniques. Since it is being widely used, it is important to search for ways to speed up the process of PSO, as the complexity of the problems increase. The paper describes a way to improve it via parallelization. The parallel PSO algorithm’s robustness and efficiency is demonstrated. This paper evaluates the parallelized version of the PSO algorithm with the use of Parallel Computing Toolbox available in Matlab.