Most existing multiobjetive 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.

翻译：为解决这一问题,提出了基于代用辅助进化算法(SAEAs)的转让学习计划,在该计划中,采用共同套件来模拟快速和慢速客观函数之间的功能关系,并采用可转移实例选择方法,以便从快速目标的搜索过程中获取有用的知识。 我们关于DTLZ和UF测试套件的实验结果表明,在目标不统一评价时间的情况下,拟议的算法具有解决双目标优化的竞争力。