Multi-Objective Particle Swarm Optimization using speciation

Particle Swarm Optimization (PSO) has been successfully extended to solve Multi-Objective Problems. These approaches are known as Multi-Objective Particle Swarm Optimizers (MOPSO). Most of the MOPSO proposes a different scheme to select the leaders used to update the velocity by using non-dominated solutions stored on an External Archive. MOPSO-CDR is one of these approaches and selects the social and the cognitive leaders based on the crowding distance. In this paper we propose a MOPSO with two distinct operation modes. The two modes are the basic mode, which is the same mode used in the MOPSO-CDR, and the speciation mode, where the swarm is divided in sub-swarms. In the latter, each swarm has a different target. The algorithm changes the operation mode based on the evaluation of the External Archive. We used well know metrics to evaluate the evolution of the Pareto Fronts, such as spacing and maximum spread. These metrics are used to determine the switching rules between the operation modes. We demonstrated that our proposal outperformed five other algorithms in five well know benchmark functions.