期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 47, 期 9, 页码 2896-2910出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2616170
关键词
Global numerical optimization; large-scale optimization; particle swarm optimization (PSO); segment-based predominant learning swarm optimizer (SPLSO)
类别
资金
- National Natural Science Foundation of China [61622206, 61379061, 61332002]
- Natural Science Foundation of Guangdong [2015A030306024]
- Guangdong Special Support Program [2014TQ01X550]
Large-scale optimization has become a significant yet challengingareainevolutionarycomputation. Tosolvethisproblem, this paper proposes a novel segment-based predominant learning swarm optimizer (SPLSO) swarm optimizer through letting several predominant particles guide the learning of a particle. First, a segment-based learning strategy is proposed to randomly divide the whole dimensions into segments. During update, variables in different segments are evolved by learning fromdifferent exemplars while theones inthesamesegmentare evolvedbythesameexemplar. Second, to accelerate search speed and enhance search diversity, a predominant learning strategy is also proposed, which lets several predominant particles guide the update of a particle with each predominant particle responsible for one segment of dimensions. By combining these two learning strategies together, SPLSOevolves all dimensions simultaneously and possesses competitive exploration and exploitation abilities. Extensive experiments are conducted on two large-scale benchmark function sets to investigate the influence of each algorithmic component and comparisons with several state-of-the-art meta-heuristic algorithms dealing with large-scale problems demonstrate the competitive efficiency and effectiveness of the proposed optimizer. Further the scalability of the optimizer to solve problems with dimensionality up to 2000 is also verified.
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