Handling Constrained Multi-Objective optimization with Objective Space Mapping to Decision Space Based on Extreme Learning Machine

Zhang, Hao; Ku, Tao; Ma, Lianbo; Yong, Yibo

doi:10.1109/cec48606.2020.9185580

Cited by 3 publications

(2 citation statements)

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“…The first stage is to find the corner points, and the second stage is to search the real CPF. Zhang et al [30] used the framework of artificial bee colony to divide the optimization process into two stages. In their first stage, fast non-dominated sorting is employed in promoting the population to reach the PF.…”

Section: Methods Of Transforming Cmops Into Other Problemsmentioning

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%

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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: Methods Of Transforming Cmops Into Other Problemsmentioning

confidence: 99%

Section: Summary Of Existing Cmoeasmentioning

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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Evolutionary constrained multi-objective optimization: a review

Liang,

Lin,

Yue

et al. 2024

Vicinagearth

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Solving constrained multi-objective optimization problems (CMOPs) is challenging due to the simultaneous consideration of multiple conflicting objectives that need to be optimized and complex constraints that need to be satisfied. To address this class of problems, a large number of constrained multi-objective evolutionary algorithms (CMOEAs) have been designed. This paper presents a comprehensive review of state-of-the-art algorithms for solving CMOPs. First, the background knowledge and concepts of evolutionary constrained multi-objective optimization are presented. Then, some classic constraint handling technologies (CHTs) are introduced, and the advantages and limitations of each CHT are discussed. Subsequently, based on the mechanisms used by these algorithms, the CMOEAs are classified into six categories, each of which is explained in detail. Following that, the benchmark test problems used to evaluate the algorithm’s performance are reviewed. Moreover, the experimental comparison and performance analysis of different types of algorithms are carried out on different test problems with different characteristics. Finally, some of the challenges and future research directions in evolutionary constrained multi-objective optimization are discussed.

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