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Swarm Intelligence: Based Cooperation Optimization of Multi-Modal Functions

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Abstract

In this paper, an advanced particle swarm optimization algorithm (PSO) is proposed to solve multi-modal function optimization problems. Multiple swarms are used for parallel search, and an artificial repulsive potential field on local search space is set up to prevent multiple swarms converging to the same areas. In addition, this paper provides a theoretical analysis of the strategy of multi-swarm parallel search in algorithms. Finally, the proposed algorithm has been tested on three benchmark functions, and the results show a superior performance compared with other PSO variants.

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Acknowledgments

The study was supported by the Special Fund for Basic Scientific Research of Central Colleges, China University of Geosciences (Wuhan). Grant no. CUGW090206.

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Authors and Affiliations

  1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, People’s Republic of China

    Qin Tang & Yi Shen

  2. School of Mathematics and Physics, China University of Geosciences, Wuhan, People’s Republic of China

    Qin Tang

  3. School of Computer, China University of Geosciences, Wuhan, People’s Republic of China

    Chengyu Hu & Wenyin Gong

  4. School of Arts and Communication, China University of Geosciences, Wuhan, People’s Republic of China

    Jianyou Zeng

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  1. Qin Tang
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  2. Yi Shen
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  3. Chengyu Hu
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  4. Jianyou Zeng
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  5. Wenyin Gong
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Correspondence to Qin Tang.

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Tang, Q., Shen, Y., Hu, C. et al. Swarm Intelligence: Based Cooperation Optimization of Multi-Modal Functions. Cogn Comput 5, 48–55 (2013). https://doi.org/10.1007/s12559-012-9144-5

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  • DOI: https://doi.org/10.1007/s12559-012-9144-5

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