关键词: Kriging模型, 权期望填充准则, 全局优化, 可行性概率

Abstract: Kriging surrogate model can effectively approximate the engineering optimization problem with black box constraints.However,there exists poor modeling accuracy and low efficiency of engineering optimization based on initial sample data.Considering above problems,a new effective global optimization algorithm was proposed,which combined the Kriging model,the weighted expectation improvement criterion and the evolutionary algorithm.Inspired by the expectation improvement criterion,this new filling criterion constructed the weight function to adjust the global and local exploration ability according to the weight function and the expected increment,so that it had the characteristics of self-adaptive adjustment according to the distance depended new sample point and then turned the local optimal solution to global optimization.Numerical and engineering cases showed that the global optimization algorithm under the new criterion and Kriging model could achieve fast solution to the optimization problem with high accuracy and good stability.

Key words: Kriging model, weighted expectation improvement criterion, global optimization problem, probability of feasibility