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Constrained multi-objective optimization via reverse search multitasking
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AI
DOI：10.1007/s11227-026-08614-2.png)
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Abstract
En 中文
Evolutionary multitasking (EMT) frameworks have attracted widespread attention in constrained multi-objective optimization problems (CMOPs). However, the challenges persist in task formulation and knowledge transfer. This paper proposes a reverse search evolutionary multitasking algorithm for constrained multi-objective optimization that includes the original problem task, a multi-layer reverse search auxiliary task, and a constraint-ignoring auxiliary task. The multi-layer reverse search auxiliary task guides the population to approach the constraint front from the infeasible regions, and effectively explores the infeasible regions near the constrained Pareto front (CPF). To further enhance the effectiveness of knowledge transfer, a multitasking optimization method is designed based on individual transfer strategy. The proposed framework has a parallel-friendly structure at the task, population, and reverse search layer levels, which provides potential for future high-performance or distributed implementations. The effectiveness of the proposed algorithm has been confirmed by experiments performed on four standard CMOP benchmark suites and 19 real-world CMOPs. The experimental results indicate the superiority of the proposed algorithm over several state-of-the-art algorithms in handling CMOPs.
Keywords:
Constrained multi-objective optimization
Multitasking optimization
Reverse search
Knowledge transfer
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