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Many-objective optimization with dynamic constraint handling for constrained optimization problems

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Abstract

In real-world applications, the optimization problems are usually subject to various constraints. To solve constrained optimization problems (COPs), this paper presents a new methodology, which incorporates a dynamic constraint handling mechanism into many-objective evolutionary optimization. Firstly we convert a COP into a dynamic constrained many-objective optimization problem (DCMaOP), which is equivalent to the COP, then the proposed many-objective optimization evolutionary algorithm with dynamic constraint handling, called MaDC, is realized to solve the DCMaOP. MaDC uses the differential evolution (DE) to generate individuals, and a reference-point-based nondominated sorting approach to select individuals. The effectiveness of MaDC is verified on 22 test instances. The experimental results show that MaDC is competitive to several state-of-the-art algorithms, and it has better global search ability than its peer algorithms.

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Acknowledgments

The research in this paper was supported by the National Natural Science Foundation of China (Nos.: 61203306, 61271140 and 61305086).

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, Hubei, People’s Republic of China

    Xi Li, Sanyou Zeng & Changhe Li

  2. School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang, 050031, Hebei, People’s Republic of China

    Xi Li & Jiantao Ma

Authors
  1. Xi Li
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  2. Sanyou Zeng
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  3. Changhe Li
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  4. Jiantao Ma
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Correspondence to Sanyou Zeng or Changhe Li.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study.

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Communicated by V. Loia.

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Li, X., Zeng, S., Li, C. et al. Many-objective optimization with dynamic constraint handling for constrained optimization problems. Soft Comput 21, 7435–7445 (2017). https://doi.org/10.1007/s00500-016-2286-8

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  • DOI: https://doi.org/10.1007/s00500-016-2286-8

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