Abstract：At present,the surrogate-assisted evolutionary algorithms(SAEA)are widely used to solve expensive optimization problems.The ensemble model strategy is widely used in SAEA,because it ensembles the characteristics of various models to improve the accuracy of model prediction.However,the computational cost that is used to train models is increases dramatically.Therefore,in this paper,an ensemble-assisted differential evolution algorithm based on historical model(EHDE)is proposed.The main work of this paper is divided into two parts：first,an ensemble model strategy based on a part of historical models and the current model is proposed that can efficiently reduce the calculation costs.Secondly,a new degree of uncertainty evaluation criterion based on euclidean distance in decision space is proposed in order to select the solution to calculate by expensive function.In order to verify the effectiveness of the proposed algorithm,the method and related algorithms are tested on CEC2005 test suit and compared.Experimental results show that the proposed algorithm is more effectively when is used for solving expensive optimization problem.
2022, Vol. 43 
