Adaptive archive-based multifactorial evolutionary algorithm for constrained multitasking optimization

doi:10.1016/j.asoc.2023.110385

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Adaptive archive-based multifactorial evolutionary algorithm for constrained multitasking optimization

2023-08-01

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龚文引 (Wenyin Gong) *

Shuijia Li

DOI：10.1016/j.asoc.2023.110385

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Abstract

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Evolutionary multitasking has attracted much attention in the field of evolutionary computing. Most of the existing multitasking evolutionary algorithms aim at solving unconstrained multitasking opti-mization problems. The study on constrained multitasking optimization problems is scarce. However, in practical applications, lots of optimization problems contain constraints. In this paper, an adaptive archive-based multifactorial evolutionary algorithm is proposed to solve constrained multitasking optimization problems. First, an archiving strategy is proposed to store infeasible solutions with better objective function values. With this strategy, useful information on infeasible solutions can be exploited to accelerate the convergence rate. Second, the random mating probability is adjusted through an adaptive strategy to facilitate positive knowledge transfer. Finally, a new mutation strategy is proposed to promote convergence by mutating some random individuals and replacing the individuals with the largest constraint violation. By comparing existing constrained multitasking evolutionary algorithms and some constrained single-task evolutionary algorithms, the results reveal the effectiveness of the proposed algorithm in solving constrained multitasking optimization problems.(c) 2023 Elsevier B.V. All rights reserved.

Keywords:

Constrained multitasking optimization

Evolutionary multitasking optimization

Multifactorial evolutionary algorithms