Wang Changlong, Wang Zijia, Kou Zheng
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.
Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China.
Biomimetics (Basel). 2024 Oct 8;9(10):604. doi: 10.3390/biomimetics9100604.
The field of evolutionary multitasking optimization (EMTO) has been a highly anticipated research topic in recent years. EMTO aims to utilize evolutionary algorithms to concurrently solve complex problems involving multiple tasks. Despite considerable advancements in this field, numerous evolutionary multitasking algorithms continue to use a single evolutionary search operator (ESO) throughout the evolution process. This strategy struggles to completely adapt to different tasks, consequently hindering the algorithm's performance. To overcome this challenge, this paper proposes multitasking evolutionary algorithms via an adaptive bi-operator strategy (BOMTEA). BOMTEA adopts a bi-operator strategy and adaptively controls the selection probability of each ESO according to its performance, which can determine the most suitable ESO for various tasks. In an experiment, BOMTEA showed outstanding results on two well-known multitasking benchmark tests, CEC17 and CEC22, and significantly outperformed other comparative algorithms.
近年来，进化多任务优化（EMTO）领域一直是备受期待的研究课题。EMTO旨在利用进化算法同时解决涉及多个任务的复杂问题。尽管该领域取得了显著进展，但许多进化多任务算法在整个进化过程中仍继续使用单一的进化搜索算子（ESO）。这种策略难以完全适应不同的任务，从而阻碍了算法的性能。为了克服这一挑战，本文提出了一种基于自适应双算子策略的多任务进化算法（BOMTEA）。BOMTEA采用双算子策略，并根据每个ESO的性能自适应地控制其选择概率，这可以为各种任务确定最合适的ESO。在一项实验中，BOMTEA在两个著名的多任务基准测试CEC17和CEC22上表现出色，并且显著优于其他对比算法。
