Ensemble of surrogates assisted particle swarm optimization of medium scale expensive problems

Abstract

Surrogate-assisted evolutionary algorithms have been commonly used in extremely expensive optimization problems. However, many existing algorithms are only effectively used in low-dimensional optimization problems and are easily trapped into a local optimum for medium scaled complex problems. An ensemble of surrogates assisted particle swarm optimization (EAPSO) algorithm is proposed in this paper to address this challenge. EAPSO algorithm uses multiple trial positions for each particle in the swarm and selects the promising positions by using the superiority and uncertainty of the ensemble simultaneously. Besides, a novel variable weight coefficient based on evolutionary state is proposed to balance exploration and exploitation. For faster convergence and avoiding wrong global attraction of models, optima of two surrogates (polynomial regression model and radial basis function model) are evaluated in the convergence state of particles. The strategies ensure large exploration space for the swarm and control the time to converge. Forty-two benchmark functions widely adopted in literatures are used to evaluate the proposed approach. Experimental results demonstrate the superiority of EAPSO algorithm compared with three popular algorithms.