An Evolutionary Multitasking Memetic Algorithm for Multiobjective Distributed Heterogeneous Welding Flow Shop Scheduling
针对多目标分布式异构焊接流水车间调度问题，提出一种基于多任务框架的模因算法，通过辅助任务和知识迁移策略提升搜索性能，实验验证了有效性。
The decomposable feature of operations in the welding shop scheduling scenario results in a vast search space, posing challenges for the design of traditional optimization algorithms. Addressing the multiobjective distributed heterogeneous welding shop scheduling problem (DHWSP), this work introduces a generalized multitasking framework. It establishes an auxiliary task by employing knowledge-and-learning-synergy neighborhood search, thereby enhancing the convergence and diversity of the original task. In this framework, an enhanced competitive swarm optimizer is adopted as the original task for DHWSP. Additionally, knowledge expression and transfer strategies are designed to expedite the comprehensive performance of each task by leveraging knowledge gained from search results. Finally, a memetic algorithm based on the multitasking framework is proposed for DHWSP. The effectiveness of the algorithm is validated through extensive experiments on 20 DHWSP instances. Numerical experimental results indicate that the proposed multitasking framework can significantly improve algorithmic comprehensive performance, demonstrating its efficacy in addressing the multiobjective DHWSP within a complex search space.