Expensive multi-objective evolutionary optimization with cooperative search of two-stage surrogate models
It has been paid more and more attention in recent years to solve expensive multi-objective optimization problems.However,it is challenging to train accurate and efficient models when the dimension of the decision space increases.Thus,expensive multi-objective evolutionary optimization with cooperative search of two-stage surrogate models(EMO-CS)is proposed in this paper for solving expensive problems.In the proposed method,a global model will be trained,before each iteration starts,to assist in speeding up the search for optimal solutions.Then a set of samples in the archive will be found and used to train a local model.The global and local models are used as an ensemble model,whose optimal solutions will be searched for and used to be selected for expensive objective evaluation based on the proposed uncertainty-based sampling criterion.Experimental results show that the proposed method performs better than four state-of-the-art algorithms on DTLZ and MaF test suites and two real-world optimization problems.
万方数据
维普
