IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i4p420-d503058.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/4/420/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/4/420/
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang Li & Lihong Guo & Yan Li & Chang Liu, 2019. "Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems," Mathematics, MDPI, vol. 7(5), pages 1-35, April.
    2. Jaszczur, Marek & Hassan, Qusay & Palej, Patryk & Abdulateef, Jasim, 2020. "Multi-Objective optimisation of a micro-grid hybrid power system for household application," Energy, Elsevier, vol. 202(C).
    3. Guozheng Li & Rui Wang & Tao Zhang & Mengjun Ming, 2018. "Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g," Energies, MDPI, vol. 11(4), pages 1-26, March.
    4. Mengjun Ming & Rui Wang & Yabing Zha & Tao Zhang, 2017. "Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm," Energies, MDPI, vol. 10(5), pages 1-15, May.
    5. Wenjie Wang & Guangdong Tian & Gang Yuan & Duc Truong Pham, 2023. "Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1065-1083, March.
    6. Luda Zhao & Bin Wang & Congyong Shen, 2021. "A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number representation," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-31, June.
    7. Fang, Yilin & Liu, Quan & Li, Miqing & Laili, Yuanjun & Pham, Duc Truong, 2019. "Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations," European Journal of Operational Research, Elsevier, vol. 276(1), pages 160-174.
    8. Jiang, Yuanchun & Liu, Yezheng & Shang, Jennifer & Yildirim, Pinar & Zhang, Qingfu, 2018. "Optimizing online recurring promotions for dual-channel retailers: Segmented markets with multiple objectives," European Journal of Operational Research, Elsevier, vol. 267(2), pages 612-627.
    9. Cheng-Long Wei & Gai-Ge Wang, 2020. "Hybrid Annealing Krill Herd and Quantum-Behaved Particle Swarm Optimization," Mathematics, MDPI, vol. 8(9), pages 1-23, August.
    10. Nebojsa Bacanin & Timea Bezdan & Eva Tuba & Ivana Strumberger & Milan Tuba, 2020. "Monarch Butterfly Optimization Based Convolutional Neural Network Design," Mathematics, MDPI, vol. 8(6), pages 1-33, June.
    11. Yadav, Deepanshu & Nagar, Deepak & Ramu, Palaniappan & Deb, Kalyanmoy, 2023. "Visualization-aided multi-criteria decision-making using interpretable self-organizing maps," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1183-1200.
    12. Chen, Chengcheng & Wang, Xianchang & Yu, Helong & Wang, Mingjing & Chen, Huiling, 2021. "Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 291-318.
    13. Juan Li & Dan-dan Xiao & Hong Lei & Ting Zhang & Tian Tian, 2020. "Using Cuckoo Search Algorithm with Q -Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location," Mathematics, MDPI, vol. 8(2), pages 1-32, January.
    14. Xiaoqi Zhao & Haipeng Qu & Wenjie Lv & Shuo Li & Jianliang Xu, 2021. "MooFuzz: Many-Objective Optimization Seed Schedule for Fuzzer," Mathematics, MDPI, vol. 9(3), pages 1-19, January.
    15. Wang, Rui & Li, Guozheng & Ming, Mengjun & Wu, Guohua & Wang, Ling, 2017. "An efficient multi-objective model and algorithm for sizing a stand-alone hybrid renewable energy system," Energy, Elsevier, vol. 141(C), pages 2288-2299.
    16. Minhee Kim & Junjae Chae, 2019. "Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path," Mathematics, MDPI, vol. 7(2), pages 1-21, February.
    17. He, Li-Jun & Ju, Xue-Wei & Zhang, Wei-Bo, 2018. "A fitness assignment strategy based on the grey and entropy parallel analysis and its application to MOEAAuthor-Name: Zhu, Guang-Yu," European Journal of Operational Research, Elsevier, vol. 265(3), pages 813-828.
    18. Sahinkoc, H. Mert & Bilge, Ümit, 2022. "A reference set based many-objective co-evolutionary algorithm with an application to the knapsack problem," European Journal of Operational Research, Elsevier, vol. 300(2), pages 405-417.
    19. Zheng, Zhi-xin & Li, Jun-qing & Duan, Pei-yong, 2019. "Optimal chiller loading by improved artificial fish swarm algorithm for energy saving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 227-243.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:4:p:420-:d:503058. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.