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Abstract
En 中文
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 or the framework, and much better results are achieved than some existed literature results. Published by Elsevier Inc.
Keywords:
genetic algorithim
neural network
hybrid strategy
simulation optimization
Journal
IF:
3.4
Papers:
2.3W
Citations:
3.3W
Organization
No organization information available
Cited Papers
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Constraint-handling in genetic algorithms through the use of dominance-based tournament selection.png)
PREAI
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