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Cooperative particle swarm optimizer with depth first search strategy for global optimization of multimodal functions

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Abstract

This paper presents a Cooperative Particle Swarm Optimizer with Depth First Search Strategy (DFS-CPSO), which has better seacrch capality than classical Particle Swarm Optimizer (PSO) in solving multimodal optimization problems. In order to improve the quality of information exchange, the Depth First Search (DFS) strategy is hybridized to Cooperative Particle Swarm Optimization(CPSO), which makes information transfer more effectively and generates better quality solution. Specifically, DFS strategy enables different components of solution vector to exchange information separately with PSO and increases the diversity of the population, so that the information of solution components could be preserved by multiple iterations in CPSO. Confirmatory experiments are performed to prove the effectiveness of employing the DFS strategy to CPSO. The comparative results demonstrate superior performance of DFS-CPSO in solving high dimensional multimodal functions than CPSO and other advanced methods.

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Funding

This work was supported by the National Science Foundation for Distinguished Young Scholars of China under grant No. 61725306,the National Natural Science Foundation of China under grant No. 62003370, the Nature Science Foundation of Hunan province (Grant No. 2021JJ30873) and Changsha Municipal Natural Science Foundation (Grant No. kq2014137).

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, Hunan, 410083, China

    Jie Wang, Yongfang Xie, Shiwen Xie & Xiaofang Chen

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  1. Jie Wang
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  2. Yongfang Xie
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  3. Shiwen Xie
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  4. Xiaofang Chen
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Correspondence to Shiwen Xie.

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Wang, J., Xie, Y., Xie, S. et al. Cooperative particle swarm optimizer with depth first search strategy for global optimization of multimodal functions. Appl Intell 52, 10161–10180 (2022). https://doi.org/10.1007/s10489-021-03005-x

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