Evolutionary computing (EC) is widely used in dealing with combinatorial optimization problems (COP). Traditional EC methods can only solve a single task in a single run, while real-life scenarios often need to solve multiple COPs simultaneously. In recent years, evolutionary multitasking optimization (EMTO) has become an emerging topic in the EC community. And many methods have been designed to deal with multiple COPs concurrently through exchanging knowledge. However, many-task optimization, cross-domain knowledge transfer, and negative transfer are still significant challenges in this field. A new evolutionary multitasking algorithm based on adaptive seed transfer (MTEA-AST) is developed for multitasking COPs in this work. First, a dimension unification strategy is proposed to unify the dimensions of different tasks. And then, an adaptive task selection strategy is designed to capture the similarity between the target task and other online optimization tasks. The calculated similarity is exploited to select suitable source tasks for the target one and determine the transfer strength. Next, a task transfer strategy is established to select seeds from source tasks and correct unsuitable knowledge in seeds to suppress negative transfer. Finally, the experimental results indicate that MTEA-AST can adaptively transfer knowledge in both same-domain and cross-domain many-task environments. And the proposed method shows competitive performance compared to other state-of-the-art EMTOs in experiments consisting of four COPs.

翻译：进化计算（EC）广泛用于处理组合优化问题（COP）。传统EC方法单次运行只能求解单个任务，而现实场景常需同时求解多个COP。近年来，进化多任务优化（EMTO）已成为EC领域的热点话题，众多方法通过知识交换并发处理多个COP。然而，多任务优化、跨领域知识迁移及负迁移仍是该领域重大挑战。本文提出一种基于自适应种子迁移的新型进化多任务算法（MTEA-AST）用于多任务组合优化。首先，提出维度统一策略以统一不同任务的维度；其次，设计自适应任务选择策略，通过捕获目标任务与其他在线优化任务的相似度，为目标任务选择合适的源任务并确定迁移强度；接着，构建任务迁移策略，从源任务中选取种子并纠正其中的不适宜知识以抑制负迁移。实验结果表明，MTEA-AST能在同域和跨域多任务环境中自适应迁移知识，在与四种组合优化问题构成的最新EMTO方法对比实验中展现出竞争力。