Research on improved multi-objective particle swarm optimization algorithms

As a novel multi-objective optimization technique, multi-objective particle swarm optimization (MOPSO) has gained much attention and some applications during the past decade. In order to enhance the performance of MOPSO on the diversity and the convergence of the solutions, this paper introduce the new methods to update the personal guide and select the global guide for each swarm members from the particle set and the Pareto front set. In order to validate the proposed method, some simulation results and comparisons with respect to several multi-objective evolutionary algorithms and MOPSO based algorithm which are representative of the state-of-the-art in this area are presented. The article concludes with a discussion of the obtained results as well as ideas for further research.