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A Population-Based Hybrid Extremal Optimization Algorithm

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Bio-Inspired Computing and Applications (ICIC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6840))

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Abstract

The extremal optimization (EO) algorithm is a kind of evolutionary algorithm which has been applied successfully in combinatorial optimization, while its application on continuous optimization encounters the problems of heavy complexity and weak exploration ability. This paper proposes a new hybrid population-based EO algorithm, named as the adaptive co-evolution population-based extremal optimization (ACPEO) algorithm, in which all individuals co-evolve adaptively with each other and the differential evolution (DE) operator is incorporated to improve the global search ability. By employing a novel evaluation method of variables, the ACPEO algorithm performs well on several kind of benchmark problems. Experimental results show that the ACPEO algorithm is robust due to the capability for solving different problems with the same parameter setting, and it is also stable because changes in the parameters’ values do not influence its performances seriously.

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Author information

Authors and Affiliations

  1. School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China

    Yu Chen, Kai Zhang & Xiufen Zou

Authors
  1. Yu Chen
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  2. Kai Zhang
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  3. Xiufen Zou
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, No. 5, Dongfeng Road, Jinshui District, 450002, Zhengzhou, Henan, China

    Yong Gan

  3. School of Electrical, Computer & Telecommunications Engineering, University of Wollongong, 2522, P.O. Box, North Wollongong, NSW, Australia

    Prashan Premaratne

  4. School of Computer Science and Engineering, Inha University, 402-751, Inchon, Korea

    Kyungsook Han

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© 2012 Springer-Verlag Berlin Heidelberg

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Chen, Y., Zhang, K., Zou, X. (2012). A Population-Based Hybrid Extremal Optimization Algorithm. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_54

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  • DOI: https://doi.org/10.1007/978-3-642-24553-4_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24552-7

  • Online ISBN: 978-3-642-24553-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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