Surrogate assisted multi-objective evolutionary algorithm are widely used to solve time-consuming multi-objective optimization problems.However,most existing modeling methods are designed to be embedded into specif-ic algorithms,and the ability to adapt to other algorithms is not strong.In order to build models adaptively according to data characteristics,a modeling method based on adaptive model selection is proposed.The main idea of this method is to adaptively select samples to establish global model or local model according to the sample characteris-tics of each objective function.In order to verify the effectiveness of the proposed modeling method,the proposed modeling method is applied to the double archiving time-consuming multi-objective optimization algorithm based on Gaussian process assistance(KAT2)and the time-consuming multi-objective optimization algorithm guided by ref-erence vector based on Gaussian process assistance(K-RVEA),and tested in dtlz test function.Experiments show that the proposed modeling method can effectively solve the time-consuming multi-objective optimization problem.
维普
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