An Evolutionary Multitasking Memetic Algorithm for Multi-Objective Distributed Heterogeneous Welding Flow Shop Scheduling

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 multi-objective 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 multi-objective DHWSP within a complex search space.