Evolutionary algorithm assisted by surrogate model in the framework of ordinal optimization and optimal computing budget allocation

作     者：Horng, Shih-Cheng Lin, Shin-Yeu 

作者机构：Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung Taiwan Chang Gung Univ Dept Elect Engn Tao Yuan 333 Taiwan 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2013年第233卷

页      面：214-229页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Council in Taiwan [NSC101-2221-E-182-040-MY3  NSC101-2221-E-324-015] 

主　　题：Evolutionary algorithm Ordinal optimization Optimal computing budget allocation Artificial neural network Stochastic simulation Combinatorial optimization 

摘      要：This work proposes an evolutionary algorithm (EA) that is assisted by a surrogate model in the framework of ordinal optimization (OO) and optimal computing budget allocation (OCBA) for use in solving the real-time combinatorial stochastic simulation optimization problem with a huge discrete solution space. For real-time applications, an off-line trained artificial neural network (ANN) is utilized as the surrogate model. EA, assisted by the trained ANN, is applied to the problem of interest to obtain a subset of good enough solutions, S. Also for real-time application, the OCBA technique is used to find the best solution in S, and this is the obtained good enough solution. Most importantly, a systematic procedure is provided for evaluating the performance of the proposed algorithm by estimating the distance of the obtained good enough solution from the optimal solution. The proposed algorithm is applied to a hotel booking limit (HBL) problem, which is a combinatorial stochastic simulation optimization problem. Extensive simulations are performed to demonstrate the computational efficiency of the proposed algorithm and the systematic performance evaluation procedure is applied to the HBL problem to quantify the goodness of the obtained good enough solution. (C) 2013 Elsevier Inc. All rights reserved.