Particle Swarm Optimization (PSO) Algorithm: Parameters Effect And Analysis

Optimization is a method of finding the optimum solution i.e. finding the maximum or minimum of a given objectives, subjected to various constraints. In the literature, various advanced optimization techniques are available out of which particle swarm optimization is one of the advanced optimization technique. Particle swarm optimization (PSO) is an efficient optimization method. Like other algorithm PSO is also population based algorithm.  The PSO is inspired by the metaphor of social interaction observed between fishes or birds. In a PSO algorithm, each particle is a candidate solution and each particle “flies” through the search space, depending on two important factors; the best position the current particle have found so far and the global best position identified from the entire population. PSO has been used in many fields such as in aerospace design, manufacturing, heat transfer and automobile. In the present work PSO algorithm is applied on standard benchmark functions such as Rastrigin function, problem with equality and inequality constraints. The particle swarm optimization algorithm is applied on different Benchmark function for tunning various parameters like inertia weight, social parameter and cognitive parameter. Results obtained by particle swarm optimization algorithm are compared with results of previous work and it is observed that the results obtained by PSO algorithm are better than the previous result.