Evaluation of selected fuzzy particle swarm optimization algorithms

Tomasz Krzeszowski,Krzysztof Wiktorowicz

doi:10.15439/2016f206

Abstract

This paper is devoted to an evaluation of selected fuzzy particle swarm optimization algorithms. Two non-fuzzy and four fuzzy algorithms are considered. The Takagi-Sugeno fuzzy system is utilized to change the parameters of these algorithms. A modified fuzzy particle swarm optimization method is proposed, in which each of the particles has its own inertia weight and coefficients of the cognitive and social components. The evaluation is based on the common nonlinear benchmark functions used for testing optimization methods. The ratings of the algorithms are assigned on the basis of the mean of the objective function and the relative success.

Highlights

P ARTICLE swarm optimization (PSO) is a stochastic optimization technique that was developed by Kennedy and Eberhart [1]
The PSO is mainly inspired by the social behavior of organisms that live and interact within large groups, for example, schools of fish, flocks of birds or swarms of bees
The goal of this study is to evaluate selected fuzzy PSO algorithms and to propose a modified fuzzy PSO algorithm

Summary

Introduction

P ARTICLE swarm optimization (PSO) is a stochastic optimization technique that was developed by Kennedy and Eberhart [1]. The PSO is mainly inspired by the social behavior of organisms that live and interact within large groups, for example, schools of fish, flocks of birds or swarms of bees. Among the PSO modifications we can distinguish algorithms that utilize fuzzy systems [2], [3], [11]–[15]. In papers [2], [11] a fuzzy system was used to dynamically modify the inertia weight. Another approach was presented in [3], where a fuzzy system is used to change the inertia weight and the coefficients of the cognitive and social components

Objectives

Results

Conclusion