Adaptive operator selection via deep reinforcement learning for decomposition-based multi-objective multi-task optimization
Abstract Multi-objective multi-task optimization (MO-MTO), which aims to simultaneously solve multiple related multi-objective problems by leveraging knowledge transfer across tasks to enhance optimization performance on each task, has emerged as a new research focus in the field of evolutionary computation. Although a...
Xuanwei Zhang, Zhiyuan Pan, Yushu Du et al.
· Complex & Intelligent System... · 0 citations