Segment-based predominant learning swarm optimizer for large-scale optimization
Yang, Qiang and Chen, Wei Neng and Gu, Tianlong and Zhang, Huaxiang and Deng, Jeremiah D. and Li, Yun and Zhang, Jun (2017) Segment-based predominant learning swarm optimizer for large-scale optimization. IEEE Transactions on Cybernetics, 47 (9). pp. 2896-2910. 7637019. ISSN 2168-2275 (https://doi.org/10.1109/TCYB.2016.2616170)
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Abstract
Large-scale optimization has become a significant yet challenging area in evolutionary computation. To solve this problem, 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 from different exemplars while the ones in the same segment are evolved by the same exemplar. 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, SPLSO evolves 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.
ORCID iDs
Yang, Qiang, Chen, Wei Neng, Gu, Tianlong, Zhang, Huaxiang, Deng, Jeremiah D., Li, Yun ORCID: https://orcid.org/0000-0002-6575-1839 and Zhang, Jun;-
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Item type: Article ID code: 65260 Dates: DateEvent1 September 2017Published26 October 2016Published Online3 October 2016AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science Department: Faculty of Engineering Depositing user: Pure Administrator Date deposited: 27 Aug 2018 12:01 Last modified: 24 Oct 2024 00:31 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/65260