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Combine LHS with MOEA to Optimize Complex Pareto Set MOPs

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Book cover Advances in Computation and Intelligence (ISICA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5370))

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Abstract

The Pareto set (PS) of real multi-objective optimization problems (MOPs) are often unknown and complex, so, it is significant for multi-objective evolutionary algorithms (MOEAs) to solve complex PS MOPs (CPS_MOPs namely). In this paper, we combined Latin hypercube sampling (LHS) with MOEA, proposed a LHS based MOEA (LHS-MOEA). We suggested two kinds of LHS-MOEA, in which LHS local search and evolutionary operator are combined to handle CPS_MOPs. Through some experiments, the results demonstrate that LHS-MOEA performs much better than the traditional prevalent MOEA — NSGA-II in solving CPS_MOPs.

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Author information

Authors and Affiliations

  1. Research Center of Evolutionary Computation and Intelligent System, Xiangtan University, 411105, Hunan, China

    Jinhua Zheng, Biao Luo, Miqing Li & Jing Li

Authors
  1. Jinhua Zheng
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  2. Biao Luo
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  3. Miqing Li
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  4. Jing Li
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Editor information

Editors and Affiliations

  1. Computation Center, Wuhan University, 430072, Wuhan, China

    Lishan Kang

  2. Faculty of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, P.R. China

    Zhihua Cai

  3. School of Computer Science, China University of Geosciences, Wu-Han, 430074, China Research Center for Space Science and Technology, China University of Geosciences, 430074, Wu-Han, China

    Xuesong Yan

  4. The University of Aizu, Tsuruga, Ikki-machi, 965-8580, Aizu-Wakamatsu City Fukushima, Japan

    Yong Liu

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© 2008 Springer-Verlag Berlin Heidelberg

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Zheng, J., Luo, B., Li, M., Li, J. (2008). Combine LHS with MOEA to Optimize Complex Pareto Set MOPs. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_12

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  • DOI: https://doi.org/10.1007/978-3-540-92137-0_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-92136-3

  • Online ISBN: 978-3-540-92137-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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