Indicator-Based Constrained Multiobjective Evolutionary Algorithms

Liu, Zhizhong; Wang, Yong; Wang, Bing-Chuan

doi:10.1109/tsmc.2019.2954491

Cited by 74 publications

(16 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ying et al [74] proposed an adaptive stochastic ranking mechanism, which dynamically adjusts the probability parameter according to the current evolution stage and the difference of individuals' violation degree. Liu et al [75] studied CMOEAs based on indicators by combining the indicator-based MOEA with CDP, the ε constrained method, and SR respectively. Gu et al [76] proposed an evolutionary algorithm based on the surrogate, in which an improved SR strategy based on fitness mechanism and adaptive probability operator was proposed.…”

Section: B Methods Based On the Separation Of Objectives And Constraintsmentioning

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

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.

show abstract

Section: B Methods Based On the Separation Of Objectives And Constraintsmentioning

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

“…Some CMOEAs are also developed based on these new CHTs. Liu et al [33] developed the indicator-based CMOEA framework, in which indicators are used to help with dealing with CMOPs. In TiGE-2 [34], a tri-goal technique is proposed in which three indicators are used for convergence, diversity and feasibility, respectively.…”

Section: Cmoeas Based On New Chtsmentioning

confidence: 99%

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

Ming

Gong

2022

Symmetry

View full text Add to dashboard Cite

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.

show abstract

“…The TiGE algorithm [17] designed two indicators for convergence and diversity and converted the constraints into the third indicator for feasibility, which was used as a three-objective evolutionary framework to solve constrained many-objective optimization problems. In [31], an indicatorbased constrained multi-objective evolutionary optimization algorithm framework was proposed, which can effectively combine indicator-based multi-objective evolutionary optimization algorithms with constraint-handling techniques to solve CMOPs.…”

Section: B Literature Reviewmentioning

confidence: 99%

A Constrained Multi/Many-Objective Particle Swarm Optimization Algorithm With a Two-Level Balance Scheme

Yang

Chen

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Constrained multi-objective optimization problems are common in practical engineering and are more difficult to handle than unconstrained problems. In general, it is necessary to find a balance between the convergence and diversity of solutions as well as the feasibility. For the constrained multi/many-objective optimization problem, a particle swarm optimization algorithm based on a two-level balance strategy is proposed. In contrast to existing views, the first level of the proposed algorithmic framework emphasizes convergence, while diversity and feasibility are considered together as the second-level scheme. An ensemble fitness ranking was used to improve the convergence of the proposed algorithm. To balance diversity and solution feasibility, the solutions are selected by combining the angles between the solutions using the constraint dominance principle. A penalty-based boundary-crossing approach is used as a utility function to calculate the fitness of the populations, which is compared with six state-of-the-art constrained multi/manyobjective evolutionary optimization algorithms on multiple constrained test suites, and the experimental results show that the proposed algorithm is highly competitive in most test problems. Furthermore, to illustrate the effect of different utility functions on the performance of the algorithm, the Chebyshev decomposition method is employed and compared with the former, and the results show that different utility functions need to be chosen to cope with problems of different characteristics.INDEX TERMS constrained multi/many-objective optimization, fitness ranking, constraint dominance principle, two-level balance scheme.

show abstract

Indicator-Based Constrained Multiobjective Evolutionary Algorithms

Cited by 74 publications

References 57 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

A Constrained Multi/Many-Objective Particle Swarm Optimization Algorithm With a Two-Level Balance Scheme

Contact Info

Product

Resources

About