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A constrained multi-objective optimization algorithm with two cooperative populations

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Abstract

Constrained multi-objective problems (CMOPs) require balancing convergence, diversity, and feasibility of solutions. Unfortunately, the existing constrained multi-objective optimization algorithms (CMOEAs) exhibit poor performance when solving the CMOPs with complex feasible regions. To solve this shortcoming, this work proposes an improved algorithm named the CMOEA-TCP, which maintains two populations cooperating to push the solutions to approximate the constrained Pareto front. Specifically, one population is obtained by the Pareto-based method and aims to strengthen the algorithm’s convergence ability. Meanwhile, another population is maintained by decomposition-based method and devoted to improving its diversity. The two populations work cooperatively during the entire evolution process with the constraint-handling technique. The performance of the CMOEA- TCP is verified on three benchmark suites with 34 problems. The experimental results demonstrate that the CMOEA-TCP can achieve performance comparable to or better than the other six state-of-the-art CMOEAs on the majority of considered problems.

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Acknowledgements

This work was financially supported by the National Key Research and Development Plan under Grant No. 2020YFB1713600 and the National Natural Science Foundation of China under Grant No. 62063021; it was also supported by the Lanzhou Science Bureau project (2018-rc-98), Public Welfare Project of Zhejiang Natural Science Foundation (LGJ19E050001), and the Project of Zhejiang Natural Science Foundation (LQ20F020011).

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Authors and Affiliations

  1. School of Computer and Communication Technology, Lanzhou University of Technology, Lanzhou, 730050, China

    Jianlin Zhang, Jie Cao, Fuqing Zhao & Zuohan Chen

  2. Gansu Engineering Research Center of Manufacturing Information, Lanzhou, 730050, China

    Jianlin Zhang, Jie Cao, Fuqing Zhao & Zuohan Chen

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  1. Jianlin Zhang
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  2. Jie Cao
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Correspondence to Jianlin Zhang.

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Zhang, J., Cao, J., Zhao, F. et al. A constrained multi-objective optimization algorithm with two cooperative populations. Memetic Comp. 14, 95–113 (2022). https://doi.org/10.1007/s12293-022-00360-1

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