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Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2 pp 167–177Cite as

A Uniform Evolutionary Algorithm Based on Decomposition and Contraction for Many-Objective Optimization Problems

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Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 2))

Abstract

For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging task. To achieve this goal, a new evolutionary algorithm based on decomposition and contraction is proposed. Moreover, a sub-population strategy is used to enhance the local search ability and improve the convergence. The proposed algorithm adopts a contraction scheme of the non-dominance area to determine the best solution of each sub-population. The comparison with the several existing well-known algorithms: NSGAII, MOEA/D and HypE, on two kinds of benchmark functions with 5 to 25 objectives is made, and the results indicate that the proposed algorithm is able to obtain more accurate Pareto front with better diversity.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Cai Dai, Yuping Wang & Lijuan Hu

Authors
  1. Cai Dai
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  2. Yuping Wang
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  3. Lijuan Hu
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Editor information

Editors and Affiliations

  1. Faculty of Science and Technology Department of Infomatics, Kindai University, Higashi-Osaka, Japan

    Hisashi Handa

  2. Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Osaka, Japan

    Hisao Ishibuchi

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Yew-Soon Ong

  4. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Kay-Chen Tan

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Dai, C., Wang, Y., Hu, L. (2015). A Uniform Evolutionary Algorithm Based on Decomposition and Contraction for Many-Objective Optimization Problems. In: Handa, H., Ishibuchi, H., Ong, YS., Tan, KC. (eds) Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-13356-0_14

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  • DOI: https://doi.org/10.1007/978-3-319-13356-0_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13355-3

  • Online ISBN: 978-3-319-13356-0

  • eBook Packages: EngineeringEngineering (R0)

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