期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 95, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2020.103905
关键词
Particle swarm optimization; Offering competition mechanism; Multimodal multi-objective; Dynamic neighborhood
类别
资金
- National Natural Science Foundation of China [U1731128]
- Natural Science Foundation of Liaoning Province, China [2019-MS-174]
- Foundation of Liaoning Province Education Administration, China [2019LNJC12]
- Graduate Science and Technology Innovation Program of USTL, China [LKDYC201921]
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 multiobjective 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 multiobjective 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.
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