期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 22, 期 5, 页码 805-817出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2017.2754271
关键词
Benchmark functions; multimodal; multiobjective; particle swarm optimization (PSO); ring topology
资金
- National Natural Science Foundation of China [61473266, 61673404, 61305080, U1304602]
- Research Award Fund for Outstanding Young Teachers in Henan Provincial Institutions of Higher Education of China [2014GGJS-004]
- Program for Science and Technology Innovation Talents in Universities of Henan Province in China [16HASTIT041, 16HASTIT033]
- Scientific and Technological Project of Henan Province [152102210153]
This paper presents a new particle swarm optimizer for solving multimodal multiobjective optimization problems which may have more than one Pareto-optimal solution corresponding to the same objective function value. The proposed method features an index-based ring topology to induce stable niches that allow the identification of a larger number of Pareto-optimal solutions, and adopts a special crowding distance concept as a density metric in the decision and objective spaces. The algorithm is shown to not only locate and maintain a larger number of Pareto-optimal solutions, but also to obtain good distributions in both the decision and objective spaces. In addition, new multimodal multiobjective optimization test functions and a novel performance indicator are designed for the purpose of assessing the performance of the proposed algorithms. An effectiveness validation study is carried out comparing the proposed method with five other algorithms using the benchmark functions to prove its effectiveness.
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