An evolutionary multitasking algorithm based on k-nearest neighbors pre-selection strategy for constrained multi-objective optimization

Evolutionary algorithms for solving constrained multi-objective optimization problems have attracted considerable attention in recent years. These algorithms typically involve population initialization and evaluation, offspring generation, and environmental selection. However, many existing algorith...

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Bibliographic Details
Published in:	Expert systems with applications Vol. 283; p. 127768
Main Authors:	Jiang, Mengqi, Gao, Xiaochuan, Dang, Qianlong, Ruan, Junhu
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 15.07.2025
Subjects:	Constrained multi-objective optimization Evolutionary multitasking K-nearest neighbors Pre-selection strategy Reverse learning mutation strategy Reverse learning mutation strategy Constrained multi-objective optimization K-nearest neighbors Pre-selection strategy Evolutionary multitasking
ISSN:	0957-4174
Online Access:	Get full text
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Summary:	Evolutionary algorithms for solving constrained multi-objective optimization problems have attracted considerable attention in recent years. These algorithms typically involve population initialization and evaluation, offspring generation, and environmental selection. However, many existing algorithms fail to improve solving efficiency due to the neglect of promising infeasible solutions. To address this issue, we propose a constrained multi-objective evolutionary algorithm that integrates a k-nearest neighbors (KNN)-based pre-selection strategy into the evolutionary multitasking framework (CMOEAKNN). Specifically, a KNN classifier is designed and trained to pre-select offspring with superior performance before performing environmental selection, thereby minimizing unnecessary evaluation efforts and retaining promising infeasible solutions, which improves the solving efficiency of the algorithm. The algorithm incorporates a reverse learning mutation strategy to improve population diversity and global exploration capability. The experiment results on three test suites and seven engineering application problems demonstrate that the proposed CMOEAKNN has significant competitiveness and superior performance compared to the other nine comparative algorithms. •A pre-selection strategy based on k-nearest neighbors has been proposed and integrated into an evolutionary multitasking framework.•The trained KNN classifier pre-selects offspring individuals, and mixes them with the offsprings for environmental selection.•A reverse learning mutation strategy is introduced, which can greatly improve the population diversity.
ISSN:	0957-4174
DOI:	10.1016/j.eswa.2025.127768