Surrogate-Assisted Expensive Many-Objective Optimization by Model Fusion

Surrogate-assisted evolutionary algorithms have played an important role in expensive optimization where a small number of real-objective function evaluations are allowed. Usually, the surrogate models are used for the same purpose, e.g., to approximate the real-objective function or the aggregation fitness function. However, there is little work on surrogate-assisted optimization by model fusion, i.e., different surrogate models are fused for different purposes to improve the performance of the algorithm. In this work, we propose a surrogate-assisted approach by model fusion for solving expensive many-objective optimization problems, in which the Kriging assisted objective function approximation method is fused with the classifier assisted approach. The proposed algorithm is compared with some state-of-the-art surrogate-assisted algorithms on DTLZ problems and a real-world problem, and some encouraging results have been achieved by our proposed model fusion based approach.