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A Constraint Partitioning Method Based on Minimax Strategy for Constrained Multiobjective Optimization Problems

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Simulated Evolution and Learning (SEAL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10593))

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Abstract

Constrained multiobjective optimization problem (CMOP) is an important research topic in the field of evolutionary computation. In terms of constraint handling, most of the existing evolutionary algorithms consider more about the proportion of infeasible solutions in population, but less concern about the distribution of infeasible solutions. Therefore, we propose a constraint partitioning method based on minimax strategy (CPM/MS) to solve CMOP. Firstly, we analyze the impact of the distribution of infeasible solutions on selecting solutions and give a preconditioning method for infeasible solutions. Secondly, we divide the preconditioned solutions into different regions by minimax strategy. Finally, we update individuals based on feasibility criteria method in each region. The effectiveness of CPM/MS algorithm is extensively evaluated on a suite of 10 bound-constrained numerical optimization problems, where the results show that CPM/MS algorithm is able to obtain considerably better fronts for some of the problems compared with some the state-of-the-art multiobjective evolutionary algorithms.

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Authors and Affiliations

  1. Dongguan University of Technology, Dongguan, 523000, Guangdong, China

    Xueqiang Li

  2. South China University of Technology, Guangzhou, 510006, Guangdong, China

    Shen Fu & Han Huang

Authors
  1. Xueqiang Li
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  2. Shen Fu
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  3. Han Huang
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Corresponding author

Correspondence to Shen Fu .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  2. City University of Hong Kong, Hong Kong, Kowloon, Hong Kong

    Kay Chen Tan

  3. Victoria University of Wellington, Wellington, Wellington, New Zealand

    Mengjie Zhang

  4. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  5. RMIT University, Melbourne, Victoria, Australia

    Xiaodong Li

  6. City University of Hong Kong, Kowloon Tong, Hong Kong

    Qingfu Zhang

  7. Peking University, Beijing, China

    Ying Tan

  8. University of Leipzig, Leipzig, Germany

    Martin Middendorf

  9. University of Surrey, Guildford, Surrey, United Kingdom

    Yaochu Jin

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Li, X., Fu, S., Huang, H. (2017). A Constraint Partitioning Method Based on Minimax Strategy for Constrained Multiobjective Optimization Problems. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_21

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  • DOI: https://doi.org/10.1007/978-3-319-68759-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68758-2

  • Online ISBN: 978-3-319-68759-9

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