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.