Most existing multiobjective evolutionary algorithms (MOEAs) implicitly assume that each objective function can be evaluated within the same period of time. Typically. this is untenable in many real-world optimization scenarios where evaluation of different objectives involves different computer simulations or physical experiments with distinct time complexity. To address this issue, a transfer learning scheme based on surrogate-assisted evolutionary algorithms (SAEAs) is proposed, in which a co-surrogate is adopted to model the functional relationship between the fast and slow objective functions and a transferable instance selection method is introduced to acquire useful knowledge from the search process of the fast objective. Our experimental results on DTLZ and UF test suites demonstrate that the proposed algorithm is competitive for solving bi-objective optimization where objectives have non-uniform evaluation times.

大多数现有的多目标进化算法 (MOEA) 都隐含地假设每个目标函数都可以在同一时间段内进行评估。通常。这在许多实际优化场景中是站不住脚的，因为对不同目标的评估涉及不同的计算机模拟或具有不同时间复杂度的物理实验。针对这一问题，提出了一种基于代理辅助进化算法（SAEA）的迁移学习方案，该方案采用协代理对快慢目标函数之间的函数关系进行建模，并引入了可迁移的实例选择方法。从快速目标的搜索过程中获取有用的知识。