Evaluation of selected fuzzy particle swarm optimization algorithms

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.