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Evolutionary Multitasking With Multiple Knowledge Representations and Elite Vector Guidance for Solving Large-Scale Multi-Objective Optimization Problems 认领 引用
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作者 Weijie Mai Zhifan Tang +2 位作者 Weili Liu Jinghui Zhong Hu Jin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第12期2553-2571,共19页
Evolutionary multitasking optimization(EMTO) can obtain beneficial knowledge for the target task from the auxiliary task to improve its performance, which has received extensive attention in scientific research and en... Evolutionary multitasking optimization(EMTO) can obtain beneficial knowledge for the target task from the auxiliary task to improve its performance, which has received extensive attention in scientific research and engineering problems. Nevertheless, faced with the widespread large-scale multi-objective optimization problems(LSMOPs), the existing EMTO literature barely involves the research of LSMOPs. More importantly, these EMTO algorithms often get trapped in local optima when dealing with LSMOPs, resulting in a slow convergence speed, which is worthy of our attention. To this end, this paper proposes an EMTO algorithm dedicated to solving LSMOPs. On the one hand, given the intricate nature of LSMOPs, we propose a knowledge domination-based knowledge transfer mechanism that can flexibly transfer knowledge from multiple knowledge representations, i.e., the information distribution and distribution distance of the task population. On the other hand, we design an elite vector-guided search strategy. Specifically, the generative adversarial network(GAN) model should first be trained within the divided populations. Then, the well-trained model is used to generate a high-quality individual for the target individual. After that, the high-quality individual is combined with the top-performing individual in the current population to find the elite vector corresponding to the target individual. Finally, the elite vector is applied to guide the target individual to accelerate convergence towards the global optimum in the high-dimensional decision space. We conduct comprehensive experimental investigations on two artificial LSMOPs suites and six real-world LSMOPs to validate the efficiency and robustness of the proposed algorithm,through comparative analysis with state-of-the-art peer algorithms. 展开更多
关键词 Elite vector evolutionary multitasking optimization(EMTO) large-scale multi-objective optimization multiple knowledge representations
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