Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic

In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO. First, benchmark mathematical functions are used to illustrate the feasibility of the proposed approach. Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO.

► A new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. ► We propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. ► Simulation results show that the proposed approach improves the performance of PSO. ► Benchmark functions are used to illustrate the feasibility of the proposed approach.