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

• Published in: Applied soft computing Vol. 61; pp. 1125 - 1138
• Main Authors: Kaedi, Marjan, Ahn, Chang Wook
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2017
• Subjects: Bayesian networks; Bayesian optimization algorithm; Probabilistic robustness evaluation; Robust optimization; Robust optimization; Probabilistic robustness evaluation; Bayesian networks; Bayesian optimization algorithm
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.03.042

[Display omitted] •We focus on Bayesian optimization algorithm (BOA) for robust optimization.•We adopt BOA for speeding up the probabilistic robustness evaluation.•The Bayesian networks are used to identify the non-robust solutions.•The non-robust solutions are detected and evaluation of their fitness is omitted.•Our method reduces the number of fitness evaluations and improves the robustness. 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.