Multiobjective Particle Swarm Optimization Based on Ideal Distance

Abstract

Recently, multiobjective particle swarm optimization (MOPSO) has been widely used in science and engineering, however how to effectively improve the convergence and distribution of the algorithm has always been a hot research topic on multiobjective optimization problems (MOPs). To solve this problem, we propose a multiobjective particle swarm optimization based on the ideal distance (IDMOPSO). In IDMOPSO, the adaptive grid and ideal distance are used to optimize and improve the selection method of global learning samples and the size control strategy of the external archive, and the fine‐tuning parameters are introduced to adjust particle flight in the swarm dynamically. Additionally, to prevent the algorithm from falling into a local optimum, the cosine factor is introduced to mutate the position of the particles during the exploitation and exploration process. Finally, IDMOPSO, several other popular MOPSOs and MOEAs were simulated on the benchmarks functions to test the performance of the proposed algorithm using IGD and HV indicators. The experimental results show that IDMOPSO has the better convergence, diversity, and excellent solution ability compared to the other algorithms.