Title of article :
Evolutionary algorithm assisted by surrogate model in the framework of ordinal optimization and optimal computing budget allocation
Author/Authors :
Shih-Cheng Horng، نويسنده , , Shin-Yeu Lin، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2013
Pages :
16
From page :
214
To page :
229
Abstract :
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.
Keywords :
Evolutionary algorithm , Ordinal optimization , Optimal computing budget allocation , Artificial neural network , stochastic simulation , Combinatorial optimization
Journal title :
Information Sciences
Serial Year :
2013
Journal title :
Information Sciences
Record number :
1215565
Link To Document :
بازگشت