A Learning Guided Parameter Setting for Constrained Multi-Objective Optimization

Fan, Zhun; Ruan, Jie; Li, Wenji; You, Yancheng; Cai, Xinye; Xu, Zelin; Yang, Zhi; Sun, Fuzan; Wang, Zhaojun; Yuan, Yutong; Li, Zhaocheng; Zhu, G.

doi:10.1109/iciai.2019.8850786

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…Vaz et al [42] designed a threestep penalty, in which three different penalty coefficients were used at different stages of evolution. Inspired by deep learning, Fan et al [43] proposed a learning-guided parameter setting method, which can adaptively generate penalty parameters. This penalty function combines constraint violation degree, objective function values, and current generation to design an exponential decay model, which is embedded into the push and pull search (PPS) [44] framework to solve CMOPs.…”

Section: A Methods Based On Penalty Functionmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

163

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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: A Methods Based On Penalty Functionmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

163

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show abstract

“…In the dynamic penalty function method proposed by Maldonado et al [10], the penalty factor undergoes gradual adjustments with changes in the population generations. Furthermore, in the learning-guided parameter tuning method proposed by Fan et al [11] an adaptively generated penalty factor significantly enhances the adaptability of the penalty factor. From this, it is evident that the penalty function method is straightforward, yet the proper setting of the penalty factor is still challenging and usually relies on human expertise.…”

Section: Constrained Multi-objective Evolutionary Optimizationmentioning

confidence: 99%

Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization

Zuo,

Xue

2024

Mathematics

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Many practical problems can be classified as constrained multi-objective optimization problems. Although various methods have been proposed for solving constrained multi-objective optimization problems, there is still a lack of research considering the integration of multiple constraint handling techniques. Given this, this paper combines the objective and constraint separation method with the multi-operator method, proposing a population feasibility state guided autonomous constrained evolutionary optimization method. This method first defines the feasibility state of the population based on both feasibility and ε feasibility of the solutions. Subsequently, a reinforcement learning model is employed to construct a mapping model between the population state and reproduction operators. Finally, based on the real-time population state, the mapping model is utilized to recommend the promising reproduction operator for the next generation. This approach demonstrates significant performance improvement for ε constrained mechanisms in constrained multi-objective optimization algorithms, and shows considerable advantages in comparison with state-of-the-art constrained multi-objective optimization algorithms.

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“…To improve the PPS framework [52] further, Fan et al [53] proposed a self-adaptive penalty-based constraint handling method and embedded it in PPS. More specifically, inspired by the learning rate in deep leaning, the penalty factor is dynamically adjusted according to the ratio of feasible solutions to a population, the constraint violation value, the target value, etc.…”

Section: ) Penalty Functionsmentioning

confidence: 99%

A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions

2020

IEEE Access

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A Learning Guided Parameter Setting for Constrained Multi-Objective Optimization

Cited by 12 publications

References 9 publications

A Survey on Evolutionary Constrained Multiobjective Optimization

A Survey on Evolutionary Constrained Multiobjective Optimization

Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization

A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions

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