A scalable coevolutionary multi-objective particle swarm optimizer

Multi-Objective Particle Swarm Optimizers (MOPSOs) are easily trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A scalable cooperative coevolution and -dominance based MOPSO (CEPSO) is proposed to address these issues. In CEPSO, Multi-objective Optimization Problems (MOPs) are decomposed in terms of their decision variables and are optimized by cooperative coevolutionary subswarms, and a uniform distribution mutation operator is adopted to avoid premature convergence. All subswarms share an external archive based on -dominance, which is also used as a leader set. Collaborators are selected from the archive and used to construct context vectors in order to evaluate particles in a subswarm. CEPSO is tested on several classical MOP benchmark functions and experimental results show that CEPSO can readily escape from local optima and optimize both low and high dimensional problems, but the number of function evaluations only increases linearly with respect to the number of decision variables. Therefore, CEPSO is competitive in solving various MOPs.