Multi-Objective Particle Swarm Optimization with Multi-Archiving Strategy

Although the principle of multi-objective particle swarm optimization is simple and the operability is strong, it is still prone to local convergence and the convergence accuracy is not high. In order to solve the above problems, we propose a multi-objective particle swarm optimization algorithm based on multi strategies and archives. This algorithm is mainly divided into three important parts. Firstly, in the phase of sorting the optimal solutions, the solution set is stored in two different archives according to different conditions; secondly, in order to increase the diversity of the optimal solutions, several strategies are adopted in updating archives and maintaining archives' scale. Finally, Gaussian perturbation strategy is applied to increase the distribution of particles and improve the quality of the optimal solution set. We compare the proposed algorithm with other algorithms and test it with different test indexes, Pareto graphs, and convergence graphs. The results show that this proposed algorithm has remarkable performance and the proposed method has advantages.