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A Particle Swarm Optimization Using Local Stochastic Search for Continuous Optimization

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Emerging Intelligent Computing Technology and Applications (ICIC 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 304))

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Abstract

The particle swarm optimizer (PSO) is a swarm intelligence based heuristic optimization technique that can be applied to a wide range of problems. After analyzing the dynamics of tranditioal PSO, this paper presents a new PSO variant based on local stochastic search strategy (LSSPSO) for performance enhancement. This is inspired by a social phenomenon that everyone wants to first exceed the nearest superior and then all superior. Specifically, LSSPSO adopts a local stochastic search to adjust inertia weight in terms of keeping a balance between the diversity and the convergence speed, aiming to improve the performance of tranditioal PSO. Experiments conducted on unimodal and multimodal test functions demonstrate the effectiveness of LSSPSO in solving multiple benchmark problems as compared to several other PSO variants.

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Author information

Authors and Affiliations

  1. Computer School, Wuhan University, Wuhan, 430072, PR China

    Jianli Ding, Jin Liu & Wenyong Dong

  2. State Key Lab. of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Science, Beijing, 100190, PR China

    Jin Liu, Wensheng Zhang & Wenyong Dong

  3. Computer Science and Information Systems, Bradley University, Illinois, USA

    Yun Wang

Authors
  1. Jianli Ding
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  2. Jin Liu
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  3. Yun Wang
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  4. Wensheng Zhang
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  5. Wenyong Dong
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, School of Electronics and Information Engineering, Tongji University, Shanghai, China

    De-Shuang Huang

  2. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  3. Department of Chemistry, University of Louisville, 2320 South Brook Street, 40292, Louisville, Kentucky, USA

    Xiang Zhang

  4. School of Electrical, Computer & Telecommunications Engineering, The University of Wollongong,, 2522, North Wollongong, NSW, Australia

    Prashan Premaratne

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© 2012 Springer-Verlag Berlin Heidelberg

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Ding, J., Liu, J., Wang, Y., Zhang, W., Dong, W. (2012). A Particle Swarm Optimization Using Local Stochastic Search for Continuous Optimization. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_8

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  • DOI: https://doi.org/10.1007/978-3-642-31837-5_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31836-8

  • Online ISBN: 978-3-642-31837-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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