Segment-Based Predominant Learning Swarm Optimizer for Large-Scale Optimization

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 part...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2017-09, Vol.47 (9), p.2896-2910
Main Authors: Qiang Yang, Wei-Neng Chen, Tianlong Gu, Huaxiang Zhang, Deng, Jeremiah D., Yun Li, Jun Zhang
Format: Article
Language:English
Subjects:
Business competition
Clustering algorithms
Computer science
Convergence
Cybernetics
Diversity reception
Evolutionary algorithms
Evolutionary computation
Global numerical optimization
Heuristic methods
large-scale optimization
Learning
Optimization
particle swarm optimization (PSO)
segment-based predominant learning swarm optimizer (SPLSO)
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Summary: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.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2016.2616170