A hybrid genetic algorithm–neural network strategy for simulation optimization

A hybrid genetic algorithm–neural network strategy for simulation optimization

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Article ID: iaor2007993
Country: Netherlands
Volume: 170
Issue: 2
Start Page Number: 1329
End Page Number: 1343
Publication Date: Nov 2005
Journal: Applied Mathematics and Computation
Authors:
Keywords: heuristics: genetic algorithms
Abstract:

Simulation optimization aims at determining the best values of input parameters, while the analytical objective function and constraints are not explicitly known in terms of design variables and their values only can be estimated by complicated analysis or time-consuming simulation. In this paper, a hybrid genetic algorithm–neural network strategy (GA–NN) is proposed for such kind of optimization problems. The good approximation performance of neural network (NN) and the effective and robust evolutionary searching ability of genetic algorithm (GA) are applied in hybrid sense, where NNs are employed in predicting the objective value, and GA is adopted in searching optimal designs based on the predicted fitness values. Numerical simulation results and comparisons based on a well-known pressure vessel design problem demonstrate the feasibility and effectiveness of the framework, and much better results are achieved than some existed literature results.

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