An Indicator-Based Evolutionary Algorithm for Large-Scale Constrained Multi-objective Optimization

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) experience a dramatic performance degradation when solving large-scale constrained multi-objective optimization problems (LSCMOPs), since they converge very slowly and easily get trapped in local optima due to the loss of div...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation p. 1
Main Authors: Zhong, Xiaoyu, Yao, Xiangjuan, Qiao, Kangjia, Gong, Dunwei, Jin, Yaochu
Format: Journal Article
Language:English
Published: IEEE 2025
Subjects:
Benchmark testing
Constrained multi-objective optimization
Convergence
differential evolution
Diversity reception
Electronic mail
Evolutionary computation
indicator
large-scale
Optimization
Scalability
Search problems
Training
variable grouping
Vectors
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Most existing constrained multi-objective evolutionary algorithms (CMOEAs) experience a dramatic performance degradation when solving large-scale constrained multi-objective optimization problems (LSCMOPs), since they converge very slowly and easily get trapped in local optima due to the loss of diversity. To enhance the efficiency of tackling LSCMOPs, this paper proposes an indicator-based evolutionary algorithm, referred to as ILCMO. In ILCMO, two complementary indicators are proposed to assess the contribution of each individual to feasibility, convergence, and diversity. The first is a feasibility-oriented indicator designed to drive the population towards the feasible regions. The second is an infeasibility-assisted dynamic indicator, which comprises two relaxed constraint boundaries. Theoretical studies demonstrate that this dynamic indicator can effectively guide the population to focus on evenly searching the infeasible regions around feasible solutions to enhance local diversity. In addition, a variable grouping-based differential evolution (VGDE) strategy, which includes a group-based intra-learning operator and a group-based inter-learning operator, is devised to improve the quality of reproduction in large-scale search spaces. The effectiveness of the proposed algorithm is validated through comprehensive experiments on four benchmarks and a microgrid dispatch problem against seven state-of-the-art algorithms.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2025.3544287