Journal of System Simulation
Abstract
Abstract: To improve the efficiency of optimization via simulation (OvS), an OvS method based on neural network is proposed. Taking advantage of the approximation ability of neural network to nonlinear input-output relationship, neural network's outputs are used as substitutes for simulation results to reduce the required simulation runs. Samples are generated by simulation according to the three proposed samples selection methods. Owning to its advantages on learning speed, network stability and parameters selection, generalized regression neural network (GRNN) is adopted to train the samples. The trained GRNN forms a regression surface that represents the relationship between simulation inputs and outputs, which makes it feasible to use GRNN output as substitutes for simulation runs. Optimization algorithms are applied to search for the best solution on the regression surface. Experiments are carried out with some typical test functions, and the feasibility and effectiveness of our method are demonstrated.
Recommended Citation
Wu, Shihui; Liu, Xiaodong; Yue, Shao; Fa, Zhang; and Yang, Minxiang
(2019)
"Optimization via Simulation Based on Neural Network,"
Journal of System Simulation: Vol. 30:
Iss.
1, Article 5.
DOI: 10.16182/j.issn1004731x.joss.201801005
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol30/iss1/5
First Page
36
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.201801005
Last Page
44
CLC
TP391.9
Recommended Citation
Wu Shihui, Liu Xiaodong, Shao Yue, Zhang Fa, Yang Minxiang. Optimization via Simulation Based on Neural Network[J]. Journal of System Simulation, 2018, 30(1): 36-44.
DOI
10.16182/j.issn1004731x.joss.201801005
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Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons