GA based meta-modeling of BPN architecture for constrained approximate optimization

Artificial neural networks (ANN) have been extensively used as global approximation tools in the context of approximate optimization. ANN traditionally minimizes the absolute difference between target outputs and approximate outputs, thereby resulting in approximate optimal solutions being sometimes actually infeasible when it is used as a meta-model for inequality constraint functions. The paper explores the development of the modified back-propagation neural network (BPN) based meta-model that ensures the constraint feasibility of approximate optimal solution. The BPN architecture is optimized via genetic algorithm (GA) to determine integer/continuous decision parameters such as the number of hidden layers, the number of neurons in a hidden layer, and interconnection weights between layers in the network. The verification of the proposed approach is examined by adopting a number of standard structural problems and an optical disk drive (ODD) suspension problem. Finally, GA based approximate optimization of suspension with optical flying head (OFH) is conducted to enhance the shock resistance capability in addition to dynamic characteristics.