Robust optimization using Bayesian optimization algorithm: Early detection of non-robust solutions

作     者：Kaedi, Marjan Ahn, Chang Wook 

作者机构：Univ Isfahan Fac Comp Engn Hezar Jerib Ave Esfahan *** Iran GIST Sch Elect Engn & Comp Sci 123 Cheomdangwagi Ro Gwangju 61005 South Korea 

出 版 物：《APPLIED SOFT COMPUTING》 (应用软计算)

年 卷 期：2017年第61卷

页      面：1125-1138页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：ICT R&D program of MSIP/IITP [2014-0-00077] IITP grant - Korea government (MSIP) [B0717-17-0070] 

主　　题：Robust optimization Bayesian optimization algorithm Bayesian networks Probabilistic robustness evaluation 

摘      要：Probabilistic robustness evaluation is a promising approach to evolutionary robust optimization;however, high computational time arises. In this paper, we apply this approach to the Bayesian optimization algorithm (BOA) with a view to improving its computational time. To this end, we analyze the Bayesian networks constructed in BOA in order to extract the patterns of non-robust solutions. In each generation, the solutions that match the extracted patterns are detected and then discarded from the process of evaluation;therefore, the computational time in discovering the robust solutions decreases. The experimental results demonstrate that our proposed method reduces computational time, while increasing the robustness of solutions. (C) 2017 Published by Elsevier B.V.