Topology-based Personal Selection in Multi-objective Particle Swarm Optimization

Particle Swarm Optimization (PSO) is a stochastic multi-point search algorithm. It was inspired by the social behavior observed in nature, such as flocks of birds and schools of fish. In recent years, multi-objective optimization by using PSO is receiving much attention. There are two difference steps from standard single objective PSO; 1) the use of archives to reserve Pareto optimal candidates, and 2) the selection of appropriate guides for multi-objective optimization. Topology is often used for standard PSO to make appropriate balance between exploration and exploitation. However, the use of topology in multi-objective PSO is not well studied. From this viewpoint, we propose a PSO model that introduces a topology-based guide selection scheme for multi-objective optimization, in this paper. The numerical simulation results show that the proposed guide selection method is effective in multi-objective optimization benchmark problems.