Improving Particle Swarm Optimization using Fuzzy Logic

Particle Swarm Optimization is a population based optimization technique that based on probability rules. In this technique each particle moves toward their best individual and group experience had occurred. Fundamental problems of a standard PSO algorithm are fall into local optimum trap and the low speed of the convergence. One of the methods to solve these problems is to combine PSO algorithm with other methods such as fuzzy logic and genetic algorithms. In this paper two PSO algorithm based on fuzzy logic are proposed. The proposed algorithms try to solve the above mentioned problems. For evaluation purpose, the proposed algorithms are tested on number of standard optimization functions. The results of experimentations have shown the superiority of the proposed algorithm over standard PSO.