A novel improvement of Kriging surrogate model

Purpose This paper aims to introduce a method based on the optimizer of the particle swarm optimization (PSO) algorithm to improve the efficiency of a Kriging surrogate model. Design/methodology/approach PSO was first used to identify the best group of trend functions and to optimize the correlation parameter thereafter. Findings The Kriging surrogate model was used to resolve the fuselage optimization of an unmanned helicopter. Practical implications - The optimization results indicated that an appropriate PSO scheme can improve the efficiency of the Kriging surrogate model. Originality/value Both the STANDARD PSO and the original PSO algorithms were chosen to show the effect of PSO on a Kriging surrogate model.