An efficient surrogate-assisted particle swarm optimization algorithm for high-dimensional expensive problems

Abstract

In this paper, an efficient surrogate-assisted particle swarm optimization algorithm is proposed to further improve the efficiency for optimization of high-dimensional expensive problems, which sometimes involve costly simulation analysis. Unlike several surrogate-assisted metaheuristic algorithms, the proposed algorithm can effectively balance the prediction ability of surrogates and the global search ability of particle swarm optimization in the optimization process. Specifically, the proposed algorithm efficiently uses the optima obtained from the global surrogate built in the entire design space and the local surrogate built in a neighbor region around the personal historical best particle to update the velocities of the particles. It should be stressed that the neighbor region partition strategy used to obtain the optimums of local surrogates is an essential aspect of the proposed algorithm. This strategy helps to obtain the predicted optima of local surrogates in the neighbor regions to guide the search of particle swarm optimization in the optimization process. In addition, the neighbor region partition strategy considers the diversity of personal historical best particles, which enables the proposed algorithm to efficiently search for different types of problems. Moreover, the optimization efficiency of the proposed algorithm can be enhanced by using the surrogate prescreening strategy. In order to validate the proposed algorithm, it is tested on several high-dimensional numerical benchmark problems and comprehensively compared with several optimization algorithms. The results show that the proposed algorithm is very promising for the optimization of high-dimensional expensive problems.