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A New Expansion of Cooperative Particle Swarm Optimization

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Neural Information Processing. Theory and Algorithms (ICONIP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6443))

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Abstract

We previously proposed multiple particle swarm optimizers with diversive curiosity (MPSOα/DC). Its main features are to introduce diversive curiosity and localized random search into MPSO to comprehensively manage the trade-off between exploitation and exploration for preventing stagnation and improving the search efficiency. In this paper, we further extend these features to multiple particle swarm optimizers with inertia weight and multiple canonical particle swarm optimizers to create two analogues, called MPSOIWα/DC and MCPSOα/DC. To demonstrate the effectiveness of these proposals, computer experiments on a suite of multidimensional benchmark problems are carried out. The obtained results show that the search performance of the MPSOα/DC is superior to that of both the MPSOIWα/DC and MCPSOα/DC, and they have better search efficiency compared to other methods such as the convenient cooperative PSO and a real-coded genetic algorithm.

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Author information

Authors and Affiliations

  1. Department of Brain Science and Engineering, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan

    Hong Zhang

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  1. Hong Zhang
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Editor information

Editors and Affiliations

  1. School of Information Technology, Murdoch University, 6150, Murdoch, WA, Australia

    Kok Wai Wong

  2. The Australian National University, 0200, Canberra, ACT, Australia

    B. Sumudu U. Mendis

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Northfields Avenue, 2522, P.O. Box, Wollongong, NSW, Australia

    Abdesselam Bouzerdoum

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Zhang, H. (2010). A New Expansion of Cooperative Particle Swarm Optimization. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_72

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  • DOI: https://doi.org/10.1007/978-3-642-17537-4_72

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17536-7

  • Online ISBN: 978-3-642-17537-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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