Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

Gu, Qian-Ping; Wang, Qian; Xiong, Neal N.; Jiang, Song; Chen, Lu

doi:10.1007/s40747-020-00249-x

Cited by 37 publications

(6 citation statements)

References 56 publications

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“…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. This strategy considers the convergence and diversity to improve the quality of candidate solutions.…”

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.

162

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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: 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.

162

View full text Add to dashboard Cite

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“…Ying et al [28] proposed an adaptive SR mechanism by dynamically controlling probability parameters based on the difference between the current evolutionary stage and the degree of individual constraint violation. Gu et al [29] proposed an enhanced SR strategy correlating fitness and probability operators, which comprehensively considers the convergence and diversity of the population to improve the quality of candidate solutions. There are various methods for adjusting evaluation indicators using SR, each with its characteristics.…”

Section: Stochastic Rankingmentioning

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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“…Designing novel evolutionary operators can be helpful to accelerate the convergence and enhance the optimization accuracy of algorithms, so as to solve EOPs more efficiently. For example, Cai et al [104] proposed a novel mutation operator that integrates the best solution indicated by different surrogates, so as to guide the mutation direction of the populations, where the effectiveness of the proposed operator is demonstrated in the experiments on high dimensional EOPs with dimensions up to 200. Gu et al [105] presented an adaptive probability operator-based stochastic ranking strategy that enhances solution quality by considering both convergence and diversity.…”

Section: Novel Operatorsmentioning

confidence: 99%

Evolutionary Computation for Expensive Optimization: A Survey

Zhan

Zhang

2022

Mach. Intell. Res.

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Expensive optimization problem (EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation (EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas for solving EOP and related real-world applications more efficiently.

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Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

Cited by 37 publications

References 56 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

Evolutionary Computation for Expensive Optimization: A Survey

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