Application of multi-objective evolutionary algorithm based on transfer learning in sliding bearing

In recent years, decomposition-based multi-objective evolutionary algorithms have gained increasing attention for solving complex optimization problems. However, existing weight vector adaptation methods often struggle to balance diversity and convergence. To address this issue, we propose a multi-objective evolutionary algorithm based on transfer learning (MOEA/D-TL), which integrates joint distribution adaptation (JDA) to coordinate the populations generated by genetic and differential operators. The key innovations of MOEA/DTL include: (1) a dual-operator framework that leverages JDA to integrate the strengths of both operators; (2) auxiliary population labeling using Pareto dominance, leveraging JDA's characteristics; and (3) sparsity-driven adaptive weight vector adjustment to refine population distribution. Extensive experiments on 44 benchmark problems demonstrate that MOEA/D-TL outperforms nine state-of-the-art algorithms, achieving a 42%-60% improvement across three performance metrics. When applied to the optimization of sliding bearings with conflicting objectives (load capacity, heat generation, and friction coefficient), MOEA/D-TL yields solutions with broader distribution and improved uniformity compared to seven other algorithms. These results validate the algorithm's capability to balance diversity and convergence effectively.