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Enhanced Comprehensive Learning Particle Swarm Optimization with Exemplar Evolution

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Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

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Abstract

Enhanced comprehensive learning particle swarm optimization (ECLPSO) is a metaheuristic recently proposed by us for global optimization. ECLPSO is balanced in exploration and exploitation; however, it still cannot satisfactorily address some complex multimodal problems. In this paper, we investigate further improving the exploration performance of ECLPSO through exemplar evolution (EE). EE encourages information exchange among different dimensions of the search space and performs mutation and selection on personal best positions that are exemplars guiding the flight of particles. EE is able to prevent the dimensions from getting stuck in stagnancy. Experimental results on various benchmark functions demonstrate that the EE strategy significantly improves the exploration performance of ECLPSO and helps ECLPSO to locate the global optimum region on all of the functions.

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Acknowledgements

This work was supported by the Jiangxi Province Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing Open Foundation Project (2016WICSIP011), the Jiangxi Province Department of Education Science and Technology Project (GJJ151099), and the National Natural Science Foundation of China (61401187).

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Authors and Affiliations

  1. Provincial Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, 289 Tianxiang Road, Nanchang, 330099, Jiangxi, China

    Xiang Yu, Yunan Liu, Xiangsheng Feng & Genhua Chen

  2. School of Information Engineering, Nanchang Institute of Technology, 289 Tianxiang Road, Nanchang, 330099, Jiangxi, China

    Xiang Yu, Yunan Liu, Xiangsheng Feng & Genhua Chen

Authors
  1. Xiang Yu
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  2. Yunan Liu
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  3. Xiangsheng Feng
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  4. Genhua Chen
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Corresponding author

Correspondence to Xiang Yu .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

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Yu, X., Liu, Y., Feng, X., Chen, G. (2017). Enhanced Comprehensive Learning Particle Swarm Optimization with Exemplar Evolution. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_76

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  • DOI: https://doi.org/10.1007/978-3-319-68759-9_76

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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