Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems

Surrogate-assisted evolutionary algorithms (SAEAs) have recently shown excellent ability in solving computationally expensive optimization problems. However, with the increase of dimensions of research problems, the effectiveness of SAEAs for high-dimensional problems still needs to be improved further. In this paper, a two-layer adaptive surrogate-assisted evolutionary algorithm is proposed, in which three different search strategies are adaptively executed during the iteration according to the feedback information which is proposed to measure the status of the algorithm approaching the optimal value. In the proposed method, the global GP model is used to pre-screen the offspring produced by the DE/current-to-best/1 strategy for fast convergence speed, and the DE/current-to-randbest/1 strategy is proposed to guide the global GP model to locate promising regions when the feedback information reaches a presetting threshold. Moreover, a local search strategy (DE/best/1) is used to guide the local GP model which is built by using individuals closest to the current best individual to intensively exploit the promising regions. Furthermore, a dimension reduction technique is used to construct a reasonably accurate GP model for high-dimensional expensive problems. Empirical studies on benchmark problems with 50 and 100 variables demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems under a limited computational budget.