Abstract
Despite the success of evolutionary algorithms (EAs) for solving multi-objective problems, most of them are based on the assumption that all objectives can be evaluated within the same period of time. However, in many real-world applications, such an assumption is unrealistic since different objectives must be evaluated using different computer simulations or physical experiments with various time complexities. To address this issue, a surrogate assisted evolutionary algorithm along with a parameter-based transfer learning (T-SAEA) is proposed in this work. While the surrogate for the cheap objective can be updated on sufficient training data, the surrogate for the expensive one is updated by either the training data set or a transfer learning approach. To find out the transferable knowledge, a filter-based feature selection algorithm is used to capture the pivotal features of each objective, and then use the common important features as a carrier for knowledge transfer between the cheap and expensive objectives. Then, the corresponding parameters in the surrogate models are adaptively shared to enhance the quality of the surrogate models. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms on the bi-objective optimization problems whose objectives have a large difference in computational complexities.