Particle swarm optimization with dynamic parameter adaptation using interval type-2 fuzzy logic for benchmark mathematical functions

Abstract

In this paper we propose a new method for dynamic parameter adaptation in particle swarm optimization (PSO). PSO is an optimization method inspired in social behaviors, which has been applied to different optimization problems and obtaining good results. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using interval type-2 fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO, and present a comparison with original approach, and PSO with dynamic parameter adaptation using type-1 fuzzy logic.