An efficient co-evolutionary particle swarm optimizer for solving multi-objective optimization problems

An efficient co-evolutionary multi-objective particle swarm optimizer named ECMPSO was proposed. ECMPSO uses dynamic multiple swarms to deal with multiple objectives, taking one objective is optimized by each swarm into account, and maintains diversity of new found non-dominated solutions via adopts a three-level particle swarm optimization(PSO) updating rule wherein the particles learn their experiences based on personal, neighborhood, and external archive. To prove the validity of the ECMPSO algorithm for solving multi-objective problems, some benchmark problems and one real-life problem are selected to validate the performance of the ECMPSO algorithm. The experiment results show that the ECMPSO algorithm is better in terms of search precision and convergence performance than other three algorithms from the literature.