Multi-objective Particle Swarm Optimization Algorithm Based on Self-update Strategy

In multi-objective particle swarm optimization (MOPSO) algorithms, improving the diversity of solutions is very difficult yet an important problem. In this paper, a new MOPSO algorithm of searching the Pareto-optimal solution is introduced, called multi-objective particle swarm optimization algorithm based on self-update strategy (SU-MOPSO). The mainly strategy of SU-MOPSO is that improving the diversity of each particle local best position (usually called pbest) to satisfy the swarm update's needs, and fundamentally enhances the diversity of Pareto set by rising the candidate quantity. The proposed SU-MOPSO algorithm has been compared with ES-MOPSO algorithm. The results demonstrate that the SU-MOPSO algorithm has gained better convergence with even distributing and diversity of Pareto set.