Density estimation for selecting leaders and mantaining archive in MOPSO

Leader selection and archive maintenance are the two key issues, which have an important impact on the performance of the obtained approximate Pareto front, to be tackled when extending Single-Objective Particle Swarm Optimization to Multi-Objective Particle Swarm Optimization (MOPSO). In this paper, a new method of density estimation is proposed for selecting leaders and maintaining archive in MOPSO. The density of a nondominated solution in archive is calculated according to the Parallel Cell Distance after the archive is mapped from Cartesian Coordinate System into Parallel Cell Coordinate System. A new MOPSO is proposed based on this method of density estimation for selecting leaders and maintaining archive to improve the performance of convergence and diversity. The experimental results show that the proposed algorithm is significantly superior to the five chosen state-of-the-art MOPSOs on 12 test problems in term of hypervolume performance indicator.