Comparison for two global optimization algorithms based on Kriging surrogate model
Received:October 13, 2014  Revised:December 31, 2014
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KeyWord:global optimization algorithm  Kriging  EGO  surrogate model  geometric global search
周昳鸣 大连理工大学 工程力学系 工业装备结构分析国家重点实验室, 大连
张君茹 大连理工大学 工程力学系 工业装备结构分析国家重点实验室, 大连
程耿东 大连理工大学 工程力学系 工业装备结构分析国家重点实验室, 大连
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      Surrogate based algorithms have been applied increasingly in the field of structural optimization.Compared with traditional optimization algorithms,surrogate based algorithms have advantages in dealing with the problems which have noise or are very time-consuming in simulation.To avoid falling into local optima,surrogate based algorithms use infill criteria to balance exploitation and exploration.This paper presents a new global optimization algorithm based on Multi-start local search with geometrical exploration (MSG),and compares it with efficient global optimization (EGO) by using several numerical problems.This paper analyzes the effects for MSG parameters and discusses the behaviors and applications for MSG and EGO.