摘要：The multiobjective optimization problems are a common problem in various fields in the real society. Therefore, solving the multiobjective optimization problems are one of the important problems studied by many researchers in recent years. From the research in recent years, it can be seen that there is still a lot of room for development of particle swarm optimization in solving multiobjective optimization problems. This paper proposes a novel multiobjective particle swarm optimization combining hypercube and distance, called HDMOPSO. The particle velocity update part in this paper uses a combination of hypercube and distance. In order to prevent the algorithm from falling into the local optimum, the part also uses the nonlinear decreasing opposite mutation strategy, which enables the particles to explore a more area. Finally, a control strategy is used for external archive to improve the convergence and diversity of the algorithm. The algorithm has been simulated in 22 test problems and compared with multiobjective particle swarm optimization algorithms (MOPSOs) and multiobjective evolutionary algorithms (MOEAs). The results show that the HDMOPSO can effectively improve the convergence and diversity, so it is an effective improvement.
