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Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

Author

Listed:
  • Gui Li

    (Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China)

  • Gai-Ge Wang

    (Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
    Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi University for Nationalities, Nanning 530006, China)

  • Shan Wang

    (Faculty of Arts and Humanities, University of Macau, Macau 999078, China
    Institute of Collaborative Innovation, University of Macau, Macau 999078, China)

Abstract

Due to the complexity of many-objective optimization problems, the existing many-objective optimization algorithms cannot solve all the problems well, especially those with complex Pareto front. In order to solve the shortcomings of existing algorithms, this paper proposes a coevolutionary algorithm based on dynamic learning strategy. Evolution is realized mainly through the use of Pareto criterion and non-Pareto criterion, respectively, for two populations, and information exchange between two populations is used to better explore the whole objective space. The dynamic learning strategy acts on the non-Pareto evolutionary to improve the convergence and diversity. Besides, a dynamic convergence factor is proposed, which can be changed according to the evolutionary state of the two populations. Through these effective heuristic strategies, the proposed algorithm can maintain the convergence and diversity of the final solution set. The proposed algorithm is compared with five state-of-the-art algorithms and two weight-sum based algorithms on a many-objective test suite, and the results are measured by inverted generational distance and hypervolume performance indicators. The experimental results show that, compared with the other five state-of-the-art algorithms, the proposed algorithm achieved the optimal performance in 47 of the 90 cases evaluated by the two indicators. When the proposed algorithm is compared with the weight-sum based algorithms, 83 out of 90 examples achieve the optimal performance.

Suggested Citation

  • Gui Li & Gai-Ge Wang & Shan Wang, 2021. "Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization," Mathematics, MDPI, vol. 9(4), pages 1-34, February.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:420-:d:503058
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    References listed on IDEAS

    as
    1. Wang, Rui & Purshouse, Robin C. & Fleming, Peter J., 2015. "Preference-inspired co-evolutionary algorithms using weight vectors," European Journal of Operational Research, Elsevier, vol. 243(2), pages 423-441.
    2. Hong-Yan Sang & Quan-Ke Pan & Pei-Yong Duan & Jun-Qing Li, 2018. "An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1337-1349, August.
    3. Gai-Ge Wang & Suash Deb & Xinchao Zhao & Zhihua Cui, 2018. "A new monarch butterfly optimization with an improved crossover operator," Operational Research, Springer, vol. 18(3), pages 731-755, October.
    Full references (including those not matched with items on IDEAS)

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