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Constrained multitasking optimization via co-evolution and domain adaptation
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AI
DOI：10.1016/j.swevo.2024.101570.png)
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Abstract
En 中文
Constrained multitasking optimization (CMTO) obtains increasing attention recently. The goal of CMTO is to handle multiple constrained tasks simultaneously. There are two limitations of existing studies on CMTO: (i) existing knowledge transfer techniques for CMTO may not work due to non -intersecting feasible domains and the unconsidered relationship between constraints and objectives; and (ii) knowledge diversity is lacking in existing CMTO algorithms because the representation of knowledge is biased to feasible solutions. To address these limitations, this paper proposes a co -evolution and domain adaptation (CEDA) method for CMTO. First, a new constraint relaxation -based domain adaptation technique for knowledge transfer is devised. Domain adaptation can effectively address the limitations imposed by non -intersecting feasible domains. In addition, as the population evolves, the knowledge representation is biased to different kinds of solutions. Second, a co -evolutionary strategy is proposed to improve the knowledge diversity. The two proposed techniques are with generality and can be readily integrated into different multitasking frameworks. In this paper, the CEDA method is combined with two popular multitasking (i.e., multifactorial-based and multi -population -based) frameworks. The constructed CEDA-based algorithms are compared with fifteen state-of-the-art algorithms on a CMTO benchmark suite and a real -world application. Experimental results demonstrate the superiority of the proposed CEDA method.
Keywords:
Constrained multitasking optimization
Multitasking optimization
Domain adaptation
Co-evolution
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