A proposed iteration optimization approach integrating backpropagation neural network with genetic algorithm

An iteration optimization approach integrating backpropagation neural network (BPNN) with genetic algorithm (GA) is proposed. The main idea of the approach is that a BPNN model is first developed and trained using fewer learning samples, then the trained BPNN model is solved using GA in the feasible region to search the model optimum. The result of verification conducted based on this optimum is added as a new sample into the training pattern set to retrain the BPNN model. Four strategies are proposed in the approach to deal with the possible deficiency of prediction accuracy due to fewer training patterns used. Specifically, in training the BPNN model, the Bayesian regularization and modified Levenberg-Marquardt algorithms are applied to improve its generalization ability and convergence, respectively; elitist strategy is adopted and simulated annealing algorithm is embedded into the GA to improve its local searching ability. The proposed approach is then applied to optimize the thickness of blow molded polypropylene bellows used in cars. The results show that the optimal die gap profile can be obtained after three iterations. The thicknesses at nine teeth peaks of the bellow molded using the optimal gap profile fall into the desired range (0.7 +/- 0.05 mm) and the usage of materials is reduced by 22%. More importantly, this optimal gap profile is obtained via only 23 times of experiments, which is far fewer than that needed in practical molding process. So the effectiveness of the proposed approach is demonstrated. (C) 2014 Elsevier Ltd. All rights reserved.