Multiobjective Multitasking Optimization With Decomposition-Based Adaptive Knowledge Transfer

Multiobjective multitasking optimization (MTO) is an emerging research direction in the evolutionary computation community, which tries to solve multiple optimization problems concurrently by utilizing shared search knowledge among related tasks. However, most existing algorithms of MTO achieve the knowledge transfer without quantifying the differences among tasks and ignore the differences in the characteristics of transfer operators, which may degrade the convergence speed. To alleviate this issue, this article proposes a multiobjective multitasking evolutionary algorithm with decomposition-based adaptive knowledge transfer (MMTEA-DAKT). Specifically, an adaptive subproblems selection method is designed, which adopts a decomposition-based framework to decompose the MTO problem into a series of single-objective optimization subproblems, aiming to adjust the proportion of knowledge transfer among all different tasks based on the improvement rate of each subproblem. Besides, an adaptive knowledge transfer strategy is devised to select the most appropriate knowledge transfer operator, which aims to improve the efficiency of knowledge transfer. To verify the effectiveness of our proposed MMTEA-DAKT, we compare it with several advanced related algorithms on three standard multiobjective multitasking test suites and the practical application of neural architecture search. The experimental results show that MMTEA-DAKT has a significant competitive advantage in solving most of the problems compared to several state-of-the-art algorithms.