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Developing Niching Algorithms in Particle Swarm Optimization

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Handbook of Swarm Intelligence

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 8))

Abstract

Niching as an important technique for multimodal optimization has been used widely in the Evolutionary Computation research community. This chapter aims to provide a survey of some recent efforts in developing state-of-the-art PSO niching algorithms. The chapter first discusses some common issues and difficulties faced when using niching methods, then describe several existing PSO niching algorithms and how they combat these problems by taking advantages of the unique characteristics of PSO. This chapter will also describe a recently proposed lbest ring topology based niching PSO. Our experimental results suggest that this lbest niching PSO compares favourably against some existing PSO niching algorithms.

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Author information

Authors and Affiliations

  1. School of Computer Science and Information Technology, RMIT University, Melbourne, Australia

    Xiaodong Li

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  1. Xiaodong Li
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Editor information

Editors and Affiliations

  1. Electrical Engineering Department, Indian Institute of Technology, Delhi , 110016, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Director of Research and Postgraduate Office, Xi’an Jiaotong-Liverpool University , 215123, Suzhou, China

    Yuhui Shi

  3. School of Electrical & Electronic Engineering, Nanyang Technological University , 639798, Singapore

    Meng-Hiot Lim

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Li, X. (2011). Developing Niching Algorithms in Particle Swarm Optimization. In: Panigrahi, B.K., Shi, Y., Lim, MH. (eds) Handbook of Swarm Intelligence. Adaptation, Learning, and Optimization, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17390-5_3

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  • DOI: https://doi.org/10.1007/978-3-642-17390-5_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17389-9

  • Online ISBN: 978-3-642-17390-5

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