A hybrid genetic pattern search augmented Lagrangian method for constrained global optimization

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• 作者：Lino Costa^a ; ^{lac@dps.uminho.pt} ; Isabel A.C.P. Espí ; rito Santo^a ; ^{iapinho@dps.uminho.pt} ; Edite M.G.P. Fernandes^b ; ^{emgpf@dps.uminho.pt}
• 关键词：Global optimization ; Augmented Lagrangian ; Genetic algorithm ; Pattern search
• 刊名：Applied Mathematics and Computation
• 出版年：2012
• 期刊代码：7_00963003
• 类别：cp
• 出版时间：15 May, 2012
• 卷：218
• 期：18
• 页码：9415-9426
• 文件大小：308 K

摘要

Hybridization of genetic algorithms with local search approaches can enhance their performance in global optimization. Genetic algorithms, as most population based algorithms, require a considerable number of function evaluations. This may be an important drawback when the functions involved in the problem are computationally expensive as it occurs in most real world problems. Thus, in order to reduce the total number of function evaluations, local and global techniques may be combined. Moreover, the hybridization may provide a more effective trade-off between exploitation and exploration of the search space. In this study, we propose a new hybrid genetic algorithm based on a local pattern search that relies on an augmented Lagrangian function for constraint-handling. The local search strategy is used to improve the best approximation found by the genetic algorithm. Convergence to an -global minimizer is proved. Numerical results and comparisons with other stochastic algorithms using a set of benchmark constrained problems are provided.