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Adaptive Grouping Brain Storm Optimization for Multimodal Optimization Problems

Adaptive Grouping Brain Storm Optimization for Multimodal Optimization Problems

Yao Peng, Zepeng Shen, Shiqi Wang
Copyright: © 2021 |Volume: 12 |Issue: 4 |Pages: 20
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781799861355|DOI: 10.4018/IJSIR.2021100105
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MLA

Peng, Yao, et al. "Adaptive Grouping Brain Storm Optimization for Multimodal Optimization Problems." IJSIR vol.12, no.4 2021: pp.81-100. http://doi.org/10.4018/IJSIR.2021100105

APA

Peng, Y., Shen, Z., & Wang, S. (2021). Adaptive Grouping Brain Storm Optimization for Multimodal Optimization Problems. International Journal of Swarm Intelligence Research (IJSIR), 12(4), 81-100. http://doi.org/10.4018/IJSIR.2021100105

Chicago

Peng, Yao, Zepeng Shen, and Shiqi Wang. "Adaptive Grouping Brain Storm Optimization for Multimodal Optimization Problems," International Journal of Swarm Intelligence Research (IJSIR) 12, no.4: 81-100. http://doi.org/10.4018/IJSIR.2021100105

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Abstract

Multimodal optimization problem exists in multiple global and many local optimal solutions. The difficulty of solving these problems is finding as many local optimal peaks as possible on the premise of ensuring global optimal precision. This article presents adaptive grouping brainstorm optimization (AGBSO) for solving these problems. In this article, adaptive grouping strategy is proposed for achieving adaptive grouping without providing any prior knowledge by users. For enhancing the diversity and accuracy of the optimal algorithm, elite reservation strategy is proposed to put central particles into an elite pool, and peak detection strategy is proposed to delete particles far from optimal peaks in the elite pool. Finally, this article uses testing functions with different dimensions to compare the convergence, accuracy, and diversity of AGBSO with BSO. Experiments verify that AGBSO has great localization ability for local optimal solutions while ensuring the accuracy of the global optimal solutions.

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