A modified particle swarm optimization for multimodal multi-objective optimization. (October 2020) - Zhang, XuWei; Liu, Hao; Tu, LiangPing
Abstract: As an effective evolutionary algorithm, particle swarm optimization (PSO) has been widely used to solve single or multi-objective optimization problems. However, the performance of PSO in solving multi-objective problems is unsatisfactory, so a variety of PSO has been proposed to enhance the performance of PSO on multi-objective optimization problems. In this paper, a modified particle swarm optimization (AMPSO) is proposed to solve the multimodal multi-objective problems. Firstly, a dynamic neighborhood-based learning strategy is introduced to replace the global learning strategy, which enhances the diversity of the population. Meanwhile, to enhance the performance of PSO, the offering competition mechanism is utilized. 11 multimodal multi-objective optimization functions are utilized to verify the feasibility and effectiveness of the proposed AMPSO. Experimental results and statistical analysis indicate that AMPSO has competitive performance compared with 5 state-of-the-art multimodal multi-objective algorithms. Highlights: Dynamic neighborhood based learning strategy is used to enhance the diversity. An offering competition mechanism is introduced to enhance the performance of AMPSO. The proposed AMPSO is employed to solve multimodal multi-objective problems. Experimental results and statistical analysis indicate that AMPSO has competitive performance.