IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0240131.html
   My bibliography  Save this article

Decomposition and adaptive weight adjustment method with biogeography/complex algorithm for many-objective optimization

Author

Listed:
  • Wang Chen
  • Zhao Guohua

Abstract

In the EMO (evolutionary multi-objective, EMO) algorithm, MaOPs (many objective optimization problems, MaOPs) are sometimes difficult to keep the balance of convergence and diversity. The decomposition based EMO developed for MaOPs has been proved to be effective, and BBO/Complex (the biogeography based optimization for complex system, BBO/Complex) algorithm is a low complexity algorithm. In this paper, a decomposition and adaptive weight adjustment based BBO/Complex algorithm (DAWA-BBO/Complex) for MaOPs is proposed. First, a new method based on crowding distance is designed to generate a set of weight vectors with good uniformly. Second, an adaptive weight adjustment method is used to solve MaOPs with complex Pareto optimal front. Subsystem space obtains a non-dominated solution by a new selection strategy. The experimental results show that the algorithm is superior to other new algorithms in terms of convergence and diversity in DTLZ benchmark problems. Finally, the algorithm is used to solve the problem of NC (numerical control machine, NC) cutting parameters, and the final optimization result is obtained by AHP (Analytic Hierarchy Process, AHP) method. The results show that the cutting speed is 10.8m/min, back cutting depth is 0.13mm, the cutting time is 504s and the cutting cost is 22.15yuan. The proposed algorithm can effectively solve the practical optimization problem.

Suggested Citation

  • Wang Chen & Zhao Guohua, 2020. "Decomposition and adaptive weight adjustment method with biogeography/complex algorithm for many-objective optimization," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-17, October.
    • Handle: RePEc:plo:pone00:0240131
    DOI: 10.1371/journal.pone.0240131
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240131
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0240131&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0240131?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Christian Lücken & Benjamín Barán & Carlos Brizuela, 2014. "A survey on multi-objective evolutionary algorithms for many-objective problems," Computational Optimization and Applications, Springer, vol. 58(3), pages 707-756, July.
    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. Chen-Yu Chang & Pei-Fang Tsai, 2022. "Multiobjective Decision-Making Model for Power Scheduling Problem in Smart Homes," Sustainability, MDPI, vol. 14(19), pages 1-13, September.
    2. Wang, Long & Wang, Tongguang & Wu, Jianghai & Chen, Guoping, 2017. "Multi-objective differential evolution optimization based on uniform decomposition for wind turbine blade design," Energy, Elsevier, vol. 120(C), pages 346-361.
    3. Wang, Zheng & Zeng, Tiansheng & Chu, Xuening & Xue, Deyi, 2023. "Multi-objective deep reinforcement learning for optimal design of wind turbine blade," Renewable Energy, Elsevier, vol. 203(C), pages 854-869.
    4. Christian Lücken & Carlos A. Brizuela & Benjamín Barán, 2022. "Clustering-based multipopulation approaches in MOEA/D for many-objective problems," Computational Optimization and Applications, Springer, vol. 81(3), pages 789-828, April.
    5. Korotkov, Vladimir & Wu, Desheng, 2021. "Benchmarking project portfolios using optimality thresholds," Omega, Elsevier, vol. 99(C).
    6. Mohamed Abouhawwash & Kalyanmoy Deb, 2021. "Reference point based evolutionary multi-objective optimization algorithms with convergence properties using KKTPM and ASF metrics," Journal of Heuristics, Springer, vol. 27(4), pages 575-614, August.
    7. Long Wang & Ran Han & Tongguang Wang & Shitang Ke, 2018. "Uniform Decomposition and Positive-Gradient Differential Evolution for Multi-Objective Design of Wind Turbine Blade," Energies, MDPI, vol. 11(5), pages 1-19, May.
    8. Hua Li & Xiangfei Qiu & Qiuyi Xi & Ruogu Wang & Gang Zhang & Yanxin Wang & Bao Zhang, 2024. "Short-Term Optimal Scheduling of Power Grids Containing Pumped-Storage Power Station Based on Security Quantification," Energies, MDPI, vol. 17(17), pages 1-26, September.
    9. 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.
    10. Korotkov, Vladimir & Wu, Desheng, 2020. "Evaluating the quality of solutions in project portfolio selection," Omega, Elsevier, vol. 91(C).
    11. Filipe Alves & Lino A. Costa & Ana Maria A. C. Rocha & Ana I. Pereira & Paulo Leitão, 2022. "The Sustainable Home Health Care Process Based on Multi-Criteria Decision-Support," Mathematics, MDPI, vol. 11(1), pages 1-19, December.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0240131. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.