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A survey on multi-objective evolutionary algorithms for many-objective problems

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  • Christian Lücken
  • Benjamín Barán
  • Carlos Brizuela

Abstract

Multi-objective evolutionary algorithms (MOEAs) are well-suited for solving several complex multi-objective problems with two or three objectives. However, as the number of conflicting objectives increases, the performance of most MOEAs is severely deteriorated. How to improve MOEAs’ performance when solving many-objective problems, i.e. problems with four or more conflicting objectives, is an important issue since a large number of this type of problems exists in science and engineering; thus, several researchers have proposed different alternatives. This paper presents a review of the use of MOEAs in many-objective problems describing the evolution of the field, the methods that were developed, as well as the main findings and open questions that need to be answered in order to continue shaping the field. Copyright Springer Science+Business Media New York 2014

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  • 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.
    • Handle: RePEc:spr:coopap:v:58:y:2014:i:3:p:707-756
    DOI: 10.1007/s10589-014-9644-1
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    References listed on IDEAS

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    1. Johannes Bader & Kalyanmoy Deb & Eckart Zitzler, 2010. "Faster Hypervolume-Based Search Using Monte Carlo Sampling," Lecture Notes in Economics and Mathematical Systems, in: Matthias Ehrgott & Boris Naujoks & Theodor J. Stewart & Jyrki Wallenius (ed.), Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems, pages 313-326, Springer.
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    Cited by:

    1. 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.
    2. 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.
    3. Korotkov, Vladimir & Wu, Desheng, 2021. "Benchmarking project portfolios using optimality thresholds," Omega, Elsevier, vol. 99(C).
    4. 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.
    5. 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.
    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. 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.
    8. 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.
    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. 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.
    12. 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.

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