A fuzzy c-means clustering based tournament selection for multiobjective optimization

This paper proposes a fuzzy c-means clustering based evolutionary algorithm called FCEA to optimize multiobjective optimization problems. FCEA firstly employs a fuzzy c-means clustering method (FCM) to discover the population distribution structure and to obtain a membership matrix of the population at each generation. Afterward, a membership based tournament selection (MBTS) operator is designed to select parents for recombination and to guide search. Comparison experiments show that the proposed FCEA outperforms MOEA/D-DE, NSGAII, SPEA2, RM-MEDA and SMS-EMOA on solving multiobjective optimization problems with complicated PF shapes. The experiments also present that MBTS significantly contributes to the performance of FCEA.