Domain Generalization-Based Dynamic Multiobjective Optimization: A Case Study on Disassembly Line Balancing
针对拆卸线平衡问题中的环境不确定性，提出一种结合元学习的领域泛化动态多目标进化算法，通过历史环境学习生成初始种群，加快新环境下的优化速度。
The objective of disassembly lines is to disassemble end-of-life products in a remanufacturing field. The disassembly line balancing problem (DLBP) considers how to allocate disassembly operations to operators on the disassembly line to optimize predetermined goals, such as cycle time. In practice, various environmental uncertainties (e.g., uncertain product quality) exist in the disassembly line. These uncertainties entail DLBP essentially a dynamic multiobjective optimization problem (DMOP). This study presents a dynamic DLBP (D-DLB) to model the effect of environmental uncertainties on the assignment of disassembly operations. Furthermore, a prediction-based dynamic optimization algorithm, termed domain generalization-based dynamic multiobjective evolutionary algorithm (DG-DMOEA), combining meta-learning with multiobjective optimization, is proposed to solve D-DLB. In DG-DMOEA, a meta-learning algorithm is employed to learn the parameters of a solution-generative model from the Pareto-optimal sets (POSs) in all historical environments. Subsequently, the solution-generative model is applied to generate a high-quality initial population that can assist multiobjective optimization algorithms in finding the POS in the new environment faster. Since no information in the new environment is required, learning can begin before the new environment arrives, significantly reducing computational time. Moreover, different solution-generative models can be designed for different DMOPs. Therefore, DG-DMOEA can thoroughly combine real-world problem properties to represent knowledge. The experimental results show that, compared with state-of-the-art methods, DG-DMOEA can considerably improve the quality of solutions and significantly enhance the ability to react quickly to environmental changes.