Utilizing Fuzzy Logic to Improve the Performance of Particle Swarm Optimization Algorithm

The Particle Swarm Optimization (PSO) Algorithm is one of the most popular and powerful meta-heuristic methods that was inspired by the groups of birds and fish. In this paper, the fuzzy logic has been used to improve the performance of the standard version of PSO algorithm (such as achieving an optimal global solution and improving convergence characteristics). In order to confirm the superiority of the proposed Fuzzy-PSO (F-PSO) algorithm, the results are compared to the PSO standard version for applying on standard benchmark functions. The overall conclusion of the results indicates the tangible advantage of the proposed method in terms of the numerical results of the fitness level, convergence curves, and relative equality at run-time.