An adaptive surrogate optimization method for expensive black-box problems with hidden constraints
A surrogate optimization method with adaptive transition search strate-gy is proposed for expensive black-box problems with hidden constraints.In the sub-steps of the transition search,a variance related to the number of evaluated points is used for the generation of trial points by random perturbation to better balance the local and global searches.In order to better approximate the real black box objective function,an adaptively combined objective surrogate model is adopted.The effectiveness of the proposed algorithm is demonstrated by the results of the numerical experiments carried out on 50 test problems.
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