Fast Heterogeneous Multiproblem Surrogates for Transfer Evolutionary Multiobjective Optimization

Transfer evolutionary multiobjective optimization (MOO) leverages the relevant knowledge from other source problems (distinct but possibly related) to assist the optimization of the target problem of interest. Multiproblem surrogates (MPSs) stack multiple source surrogates to reduce the number of function evaluations of the target expensive problem. The current MPSs only considers several source problems and the source and target problems are assumed to be homogeneous. In order to address the above issues, this article proposes fast heterogeneous MPSs (HeMPSs) for transfer evolutionary MOO with a large number of surrogates. First, an iterative surrogate selection strategy is designed to select the highly relevant surrogates from the large-scale surrogate pool to avoid negative transfer. Second, HeMPSs are established to align the features of the source and target models. Finally, an adaptive k-fold cross-validation method is proposed to obtain the predicted values of the target model with low-computational costs. Experiments on the MOO benchmark problems and multiobjective neural architecture search problems have demonstrated that the proposed method is able to avoid negative transfer in the large-scale scenarios and reduce the computational costs.