An angle based evolutionary algorithm with infeasibility information for constrained many-objective optimization

Wang, Chao; Xu, Ran

doi:10.1016/j.asoc.2019.105911

Cited by 29 publications

(11 citation statements)

References 45 publications

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“…In addition, Ning et al [60] proposed an improved hybrid multi-objective optimization algorithm to solve CMOPs, in which each solution is given a constrained non-dominated rank according to its constraint violation degree and Pareto rank. Wang and Xu [27] used the ACDP to solve the constrained many-objective optimization problem, in which individuals with good diversity will be first selected to cope with the challenge caused by the large infeasible obstacles.…”

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

confidence: 99%

“…1, we use a taxonomy referring to the CHTs instead of the type of MOEAs. The existing CMOEAs can be divided into seven categories: 1) methods based on penalty function [24,25]; 2) methods based on the separation of objectives and constraints [26,27]; 3) multi-objective methods [28,29]; 4) methods of transforming CMOPs into other problems [19,30]; 5) hybrid methods [31,32]; 6) methods of altering the reproduction operators [33,34]; and 7) other methods.…”

Section: Summary Of Existing Cmoeasmentioning

confidence: 99%

“…Li et al [89] proposed the C-TAEA algorithm and applied it to optimize the water distribution network. Wang and Xu [27] proposed a method, named C-AnEA, to solve the five-objective water resource management problem. Qi et al [172] proposed an adaptive CMOEA based on the multi-algorithm (AMAL-GAM), then employed it to solve the multi-objective optimization design of groundwater remediation in non-aqueous liquid contaminated sites.…”

Section: Resource Optimization Problemsmentioning

confidence: 99%

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A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

165

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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%

Section: Summary Of Existing Cmoeasmentioning

confidence: 99%

Section: Resource Optimization Problemsmentioning

confidence: 99%

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A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

165

View full text Add to dashboard Cite

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“…The third case considers both environmental selection and dominance relation. For example, Wang and Xu addressed the constrained problem with angle-based constrained dominance relation and angle-based diversity estimation [51]. In their method, it is obvious that the angle-based technique can not only intuitively reflect solution diversity, but also represent the (constraint) dominance relationship.…”

Section: B Review Of Angle-based Multi-objective Optimizationmentioning

confidence: 99%

“…As shown in many works [48], [51], the traditional crowding angle is defined as the angle between two adjacent vectors, denoted as α(x i , x j ), using the following equation:…”

Section: A Interval Crowding Anglementioning

confidence: 99%

An Angle-Based Bi-Objective Optimization Algorithm for Redundancy Allocation in Presence of Interval Uncertainty

Yang

et al. 2023

IEEE Trans. Automat. Sci. Eng.

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Uncertainty is a practical issue in system design optimization because some characteristics of components, such as reliability and cost, cannot be determined precisely in many situations. Considering the imprecise characteristics of components, few works have focused on the multi-objective optimization for the redundancy allocation due to the challenges of comparing multi intervals. To tackle the issue, a novel angle-based bi-objective redundancy allocation algorithm is proposed in this study, introducing three original contributions: 1) An angle-based interval crowding distance (ICA) is especially designed for effective performance and reduced computational time; 2) Two techniques are applied to tackle the problem: An elite selection for mutation is presented for generating better offsprings; A penalty-guided constraint handling technique is introduced for converting the problem into an unconstrained one. 3) Since a set of optimal solutions is obtained by the proposed method and no preference on uncertainties is provided, this paper proposes a novel knee interval method to help DMs make a decision. To be specific, the proposed ICA can describe the distribution of the whole population intuitively and effectively, considering not only the angle between two compared individuals but also the angle range of the interval values. The computational results from two typical experiments demonstrate that the proposed algorithm is more efficient than other state-of-the-art algorithms, generating Pareto sets with less repeating individuals, stronger convergence, wider distribution, less imprecision, and reduced computational time.Note to Practitioners-This article is motivated by two practical problems in multi-objective redundancy allocation in presence of interval uncertainty: First, this paper tries to solve the multiobjective redundancy allocation problem with the imprecise characteristics of components, which is rarely considered in the field of reliability optimization design. Second, the calculation of the crowding distance needs extra time cost and is less efficient. To tackle this issue, an interval crowding angle is especially designed, considering not only the angle between two compared individuals, but also the angle range of the interval values. The proposed method can be embedded in most multi-objective

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