Handling Constrained Multiobjective Optimization Problems via Bidirectional Coevolution

Liu, Zhizhong; Wang, Bing-Chuan; Tang, Ke

doi:10.1109/tcyb.2021.3056176

Cited by 86 publications

(27 citation statements)

References 55 publications

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“…The normal population searches the whole CPF and prioritizes the feasibility and the diversity of the population. A bi-directional coevolution algorithm was designed by Liu et al, in which the main population and an archive population are employed simultaneously [91]. Specifically, the main population keeps the feasibility and moves from the feasible side to the CPF, while the archive population uses angle information to maintain population diversity and approximate the CPF from the infeasible side.…”

Section: Methods Of Transforming Cmops Into Other Problemsmentioning

confidence: 99%

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A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

111

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Handling constrained multi-objective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multi-objective optimization. We first review a large number of CMOEAs through categorization, and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multi-objective optimization.

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Section: Methods Of Transforming Cmops Into Other Problemsmentioning

confidence: 99%

“…In order to analyze the performance of CMOEAs on different types of problems, five representative CMOEAs are selected, which are BiCo [91], C-TAEA [89], CCMO [88], MOEA/D-DAE [71], and PPS-MOEA/D [19]. These CMOEAs have achieved exceedingly competitive performance on CMOPs and are widely used in comparison experiments.…”

Section: B Comparison Of Different Cmoeasmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

111

View full text Add to dashboard Cite

show abstract

“…Yao et al [20] proposed a two archive framework in which the convergence archive aims to promote convergence and feasibility while the diversity archive aims to promote diversity. Liu et al [24] proposed a bidirectional coevolution framework in which one population approximates constrained Pareto front (CPF) from the feasible regions and the other from the infeasible regions. Tian et al [17] proposed a weak coevolutionary framework in which one population solves the original CMOP while the other solves the constraints-ignored helper problem, and the two coevolved populations generate offspring on their own.…”

Section: Cmoeas Based On Constrained Dominance Principlementioning

confidence: 99%

An Ensemble Framework of Evolutionary Algorithm for Constrained Multi-Objective Optimization

Ming

Gong

2022

Symmetry

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In the real-world, symmetry or asymmetry widely exists in various problems. Some of them can be formulated as constrained multi-objective optimization problems (CMOPs). During the past few years, handling CMOPs by evolutionary algorithms has become more popular. Lots of constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been proposed. Whereas different CMOEAs may be more suitable for different CMOPs, it is difficult to choose the best one for a CMOP at hand. In this paper, we propose an ensemble framework of CMOEAs that aims to achieve better versatility on handling diverse CMOPs. In the proposed framework, the hypervolume indicator is used to evaluate the performance of CMOEAs, and a decreasing mechanism is devised to delete the poorly performed CMOEAs and to gradually determine the most suitable CMOEA. A new CMOEA, namely ECMOEA, is developed based on the framework and three state-of-the-art CMOEAs. Experimental results on five benchmarks with totally 52 instances demonstrate the effectiveness of our approach. In addition, the superiority of ECMOEA is verified through comparisons to seven state-of-the-art CMOEAs. Moreover, the effectiveness of ECMOEA on the real-world problems is also evaluated for eight instances.

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“…In addition, to take advantage of the complementary effects of both CA and DA, a restricted mating selection mechanism was proposed to adaptively choose appropriate mating parents according to the evolution status of the CA and DA respectively. After [26], there have been a spike of efforts on the development of multi-population strategies (e.g., [27][28][29][30][31][32]) to leverage some complementary effects of both feasible and infeasible solutions simultaneously for solving CMOPs.…”

Section: Introductionmentioning

confidence: 99%

Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-Objective Optimization?

Li¹,

Li²,

Li³

2022

Preprint

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Constraint violation has been a building block to design evolutionary multi-objective optimization algorithms for solving constrained multi-objective optimization problems. However, it is not uncommon that the constraint violation is hardly approachable in real-world black-box optimization scenarios. It is unclear that whether the existing constrained evolutionary multi-objective optimization algorithms, whose environmental selection mechanism are built upon the constraint violation, can still work or not when the formulations of the constraint functions are unknown. Bearing this consideration in mind, this paper picks up four widely used constrained evolutionary multi-objective optimization algorithms as the baseline and develop the corresponding variants that replace the constraint violation by a crisp value. From our experiments on both synthetic and real-world benchmark test problems, we find that the performance of the selected algorithms have not been significantly influenced when the constraint violation is not used to guide the environmental selection.

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Handling Constrained Multiobjective Optimization Problems via Bidirectional Coevolution

Cited by 86 publications

References 55 publications

A Survey on Evolutionary Constrained Multiobjective Optimization

A Survey on Evolutionary Constrained Multiobjective Optimization

An Ensemble Framework of Evolutionary Algorithm for Constrained Multi-Objective Optimization

Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-Objective Optimization?

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