Even Search in a Promising Region for Constrained Multi-Objective Optimization

Fei Ming; Wenyin Gong; Yaochu Jin

doi:10.1109/JAS.2023.123792

Volume 11 Issue 2

Feb. 2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2024 > 11(2): 474-486

F. Ming, W. Gong, and Y. Jin, “Even search in a promising region for constrained multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 474–486, Feb. 2024. doi: 10.1109/JAS.2023.123792

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F. Ming, W. Gong, and Y. Jin, “Even search in a promising region for constrained multi-objective optimization,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 474–486, Feb. 2024. doi: 10.1109/JAS.2023.123792

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Even Search in a Promising Region for Constrained Multi-Objective Optimization

doi: 10.1109/JAS.2023.123792

Fei Ming^,,
Wenyin Gong^{,
,},
Yaochu Jin^{,
,}

Funds: This work was partly supported by the National Natural Science Foundation of China (62076225)

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Author Bio:
Fei Ming received the B.Sc. degree in computer science from China University of Geosciences in 2019. He is currently a Ph.D. candidate with the School of Computer Science, China University of Geosciences. His current research interests include evolutionary multiobjective optimization methods and their applications

Wenyin Gong (Member, IEEE) received the B.Eng., M.Eng. and Ph.D. degrees in computer science from China University of Geosciences, in 2004, 2007 and 2010, respectively. He is currently a Professor with School of Computer Science, China University of Geosciences. His research interests include evolutionary algorithms, evolutionary optimization, and their applications. Professor Gong has published over 80 research papers in journals and international conferences. He served as a Referee for over 30 international journals, such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Systems, MAN, and Cybernetics: Systems, IEEE Computational Intelligence Magazine, ACM Transactions on Intelligent Systems and Technology, Information Sciences, European Journal of Operational Research, Applied Soft Computing, Journal of Power Sources, etc. He currently serves as Associate Editor of Expert Systems With Applications, International Journal of Bio-Inspired Computation, Memetic Computing, etc

Yaochu Jin (Fellow, IEEE) received the B.Sc., M.Sc., and Ph.D. degrees in automatic control from Zhejiang University, in 1988, 1991 and 1996, respectively, and the Dr.-Ing, degree in neuroinformatics from Ruhr University Bochum, Germany in 2001. He joined the Westlake University in October 2023 as a Chair Professor of Al, leading the Trustworthy and General Al Laboratory. He was an Alexander von Humboldt Professor for Al with the Faculty of Technology, Bielefeld University, Germany from 2021 to 2023, and a Distinguished Chair, Professor in Computational Intelligence with the Department of Computer Science, University of Surrey, UK. He was a “Finland Distinguished Professor” with the University of JyvÃskyla, Finland, “Changjiang Distinguished Visiting Professor” with Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include multi-objective and data-driven evolutionary optimization, evolutionary multi-objective learning, trustworthy Al, and evolutionary developmental Al. Professor Jin is presently the President of the IEEE Computational Intelligence Society and the Editor-in-Chief of Complex & Intelligent Systems. He is the recipient of the 2018, 2021 and 2023 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award. He was named by the Clarivate as “a Highly Cited Researcher” from 2019 to 2023 consecutively. He is a Member of Academia Europaea and Fellow of IEEE
Corresponding author: Wenyin Gong, e-mail: wygong@cug.edu.cn; Yaochu Jin, e-mail: jinyaochu@westlake.edu.cn
¹ Overfit in this work indicates that a method performs extremely well on some problems but unusable (i.e., cannot find a desirable number of applicable solutions) on others.
² Evidence can be found in Secion IV, where we perform various experiments to test performances of different CMOEAs on different CMOPs. The results clearly reveal that some CMOEAs using a specific or complicated strategy (e.g., BiCo and MFOSPEA2) perform very well on one kind of CMOP but cannot handle other features of CMOPs.
³ Detailed discussions and analyses on all experimental results can be found in the supplementary file at: https: //wewnyin.github.io/wenyingong/Publication/CMOES-supp.pdf.
⁴ Dimensions of m and n of the selected benchmarks are reported in Table S-I in the Supplementary file.
Received Date: 2023-06-18
Accepted Date: 2023-07-25

Available Online: 2023-11-23

Abstract

Abstract

In recent years, a large number of approaches to constrained multi-objective optimization problems (CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly fine-tuned strategy or technique might overfit some problem types, resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties. First, the constrained Pareto front (CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance (i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search, which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.
- Constrained multi-objective optimization,
- even search,
- evolutionary algorithms,
- promising region,
- real-world problems

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¹ Overfit in this work indicates that a method performs extremely well on some problems but unusable (i.e., cannot find a desirable number of applicable solutions) on others.
² Evidence can be found in Secion IV, where we perform various experiments to test performances of different CMOEAs on different CMOPs. The results clearly reveal that some CMOEAs using a specific or complicated strategy (e.g., BiCo and MFOSPEA2) perform very well on one kind of CMOP but cannot handle other features of CMOPs.
³ Detailed discussions and analyses on all experimental results can be found in the supplementary file at: https: //wewnyin.github.io/wenyingong/Publication/CMOES-supp.pdf.
⁴ Dimensions of m and n of the selected benchmarks are reported in Table S-I in the Supplementary file.

References(31)

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A promising region concept that includes the CPF and possesses good properties
An even search method utilizing valuable solutions in the promising region to search the CPF
A new two-stage CMOEA that implements the even search method

Even Search in a Promising Region for Constrained Multi-Objective Optimization

doi: 10.1109/JAS.2023.123792

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