{"doi":"10.1108/aeat-06-2018-0157","title":"A novel improvement of Kriging surrogate model","abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose</jats:title>\n<jats:p>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.</jats:p>\n</jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design/methodology/approach</jats:title>\n<jats:p>PSO was first used to identify the best group of trend functions and to optimize the correlation parameter thereafter.</jats:p>\n</jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings</jats:title>\n<jats:p>The Kriging surrogate model was used to resolve the fuselage optimization of an unmanned helicopter.</jats:p>\n</jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications</jats:title>\n<jats:p>The optimization results indicated that an appropriate PSO scheme can improve the efficiency of the Kriging surrogate model.</jats:p>\n</jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality/value</jats:title>\n<jats:p>Both the STANDARD PSO and the original PSO algorithms were chosen to show the effect of PSO on a Kriging surrogate model.</jats:p>\n</jats:sec>","journal":"Aircraft Engineering and Aerospace 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of an unmanned helicopter.</jats:p>\n</jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications</jats:title>\n<jats:p>The optimization results indicated that an appropriate PSO scheme can improve the efficiency of the Kriging surrogate model.</jats:p>\n</jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality/value</jats:title>\n<jats:p>Both the STANDARD PSO and the original PSO algorithms were chosen to show the effect of PSO on a Kriging surrogate 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