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- W2362552213 abstract "Three kinds of training algorithms for efficient global optimization(EGO) method are investigated.A kind of training algorithm based on low-discrepancy sequences is proposed to reduce randomness of EGO method.Performance of EGO method depends on a good training algorithm.Since training problems in EGO are non-convex and non-smooth,meta-heuristic algorithms,random algorithm and low-discrepancy sequences are chosen to address five benchmark optimization problems and two aerodynamic shape optimization problems.In these problems,differential evolution algorithm was found the best in meta-heuristic algorithms.Training algorithm based on low-discrepancy sequences can effectively reduce randomness of EGO method and Faure sequence has the best performance." @default.
- W2362552213 created "2016-06-24" @default.
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- W2362552213 date "2012-01-01" @default.
- W2362552213 modified "2023-09-25" @default.
- W2362552213 title "Training Algorithms for EGO Method and Applications" @default.
- W2362552213 hasPublicationYear "2012" @default.
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