Dynamic multiobjective optimization problems (DMOPs) are characterized by optimization functions that change over time in varying environments. The DMOP is challenging because it requires the varying Pareto-optimal sets (POSs) to be tracked quickly and accurately during the optimization process. In recent years, transfer learning has been proven to be one of the effective means to solve dynamic multiobjective optimization. However, the negative transfer will lead the search of finding the POS to a wrong direction, which greatly reduces the efficiency of solving optimization problems. Minimizing the occurrence of negative transfer is thus critical for the use of transfer learning in solving DMOPs. In this article, we propose a new individual-based transfer learning method, called an individual transfer-based dynamic multiobjective evolutionary algorithm (IT-DMOEA), for solving DMOPs. Unlike existing approaches, it uses a presearch strategy to filter out some high-quality individuals with better diversity so that it can avoid negative transfer caused by individual aggregation. On this basis, an individual-based transfer learning technique is applied to accelerate the construction of an initial population. The merit of the IT-DMOEA method is that it combines different strategies in maintaining the advantages of transfer learning methods as well as avoiding the occurrence of negative transfer; thereby greatly improving the quality of solutions and convergence speed. The experimental results show that the proposed IT-DMOEA approach can considerably improve the quality of solutions and convergence speed compared to several state-of-the-art algorithms based on different benchmark problems.

中文翻译：

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用于动态多目标优化的基于个体的迁移学习

动态多目标优化问题 (DMOP) 的特点是优化函数在不同环境中随时间变化。DMOP 具有挑战性，因为它需要在优化过程中快速准确地跟踪变化的帕累托最优集 (POS)。近年来，迁移学习已被证明是解决动态多目标优化问题的有效手段之一。但是负迁移会导致寻找POS的搜索方向错误，大大降低了求解优化问题的效率。因此，最大限度地减少负迁移的发生对于使用迁移学习解决 DMOP 至关重要。在本文中，我们提出了一种新的基于个体的迁移学习方法，称为基于个体转移的动态多目标进化算法 (IT-DMOEA)，用于解决 DMOP。与现有方法不同，它使用预搜索策略过滤掉一些具有更好多样性的高质量个体，从而避免个体聚集引起的负迁移。在此基础上，应用基于个体的迁移学习技术来加速初始种群的构建。IT-DMOEA方法的优点在于它结合了不同的策略，既保持了迁移学习方法的优点，又避免了负迁移的发生；从而大大提高解的质量和收敛速度。