Indicator-Based Evolutionary Algorithm for Solving Constrained Multiobjective Optimization Problems

Yuan, Jiawei; Liu, Hai Lin; Ong, Yew-Soon; He, Zhaoshui

doi:10.1109/tevc.2021.3089155

Cited by 73 publications

(19 citation statements)

References 40 publications

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“…Xiang et al [35] proposed a new CHT which works for the CPF shape detection mechanism to detect problem type. Yuan et al [15] proposed a new cost value based indicator of which the convergence to CPF property is proofed.…”

Section: Cmoeas Based On New Chtsmentioning

confidence: 99%

“…In the generated ECMOEA, ICMA [15], CCMO [17] and PPS [22] are embedded as the candidates. Therefore, these three CMOEAs are chosen as comparisons in Section 4.2.…”

Section: Experimental Settings 411 Cmoeas and Cmops In Comparisonmentioning

confidence: 99%

“…They mainly include the dominance-based [12], decomposition-based [13] and indicator-based [14] ones. During the past two decades, lots of CMOEAs have been proposed [15][16][17], and the constrained multi-objective optimization (CMO) community has made great progress. However, despite the prosperity of CMO research field, these CMOEAs all have their own strengths and weaknesses [18].…”

Section: Introductionmentioning

confidence: 99%

“…Three state-of-the-art CMOEAs (CCMO [17], ICMA [15] and PPS [22]) have been successfully embedded into the proposed framework and a new CMOEA termed ECMOEA is developed. Experimental results have demonstrated the effectiveness of the proposed framework.…”

mentioning

confidence: 99%

“…Since PPS is the most unsuitable CMOEA, is obtained the minimum HV value. Therefore, at the first decision time, PPS is deleted (lines 9-12).Then, since t 1 is set 1, during 0.5 × g max < g < 0.8 × g max , ICMA and CCMO are co-evolved (lines [14][15]. When g = 0.8 × g max , it comes to the second decision time (lines 18-21).…”

mentioning

confidence: 99%

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

Ming

Gong

2022

Symmetry

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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.

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Section: Cmoeas Based On New Chtsmentioning

confidence: 99%

“…In the generated ECMOEA, ICMA [15], CCMO [17] and PPS [22] are embedded as the candidates. Therefore, these three CMOEAs are chosen as comparisons in Section 4.2.…”

Section: Experimental Settings 411 Cmoeas and Cmops In Comparisonmentioning

confidence: 99%