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Hybrid Multi-Population and Adaptive Search Range Strategy With Particle Swarm Optimization for Multimodal Optimization

Hybrid Multi-Population and Adaptive Search Range Strategy With Particle Swarm Optimization for Multimodal Optimization

Shiqi Wang, Zepeng Shen, Yao Peng
Copyright: © 2021 |Volume: 12 |Issue: 4 |Pages: 23
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781799861355|DOI: 10.4018/IJSIR.2021100108
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MLA

Wang, Shiqi, et al. "Hybrid Multi-Population and Adaptive Search Range Strategy With Particle Swarm Optimization for Multimodal Optimization." IJSIR vol.12, no.4 2021: pp.146-168. http://doi.org/10.4018/IJSIR.2021100108

APA

Wang, S., Shen, Z., & Peng, Y. (2021). Hybrid Multi-Population and Adaptive Search Range Strategy With Particle Swarm Optimization for Multimodal Optimization. International Journal of Swarm Intelligence Research (IJSIR), 12(4), 146-168. http://doi.org/10.4018/IJSIR.2021100108

Chicago

Wang, Shiqi, Zepeng Shen, and Yao Peng. "Hybrid Multi-Population and Adaptive Search Range Strategy With Particle Swarm Optimization for Multimodal Optimization," International Journal of Swarm Intelligence Research (IJSIR) 12, no.4: 146-168. http://doi.org/10.4018/IJSIR.2021100108

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Abstract

This paper proposes an algorithm named hybrid multi-population and adaptive search range strategy with particle swarm optimization (ARPSO) for solving multimodal optimization problems. The main idea of the algorithm is to divide the global search space into multiple sub-populations searching in parallel and independently. For diversity increasing, each sub-population will continuously change the search area adaptively according to whether there are local optimal solutions in its search space and the position of the global optimal solution, and in each iteration, the optimal solution in this area will be reserved. For the purpose of accelerating convergence, at the global and local levels, when the global optimal solution or local optimal solution is found, the global search space and local search space will shrink toward the optimal solution. Experiments show that ARPSO has unique advantages for solving multi-dimensional problems, especially problems with only one global optimal solution but multiple local optimal solutions.

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