Optimization method by combination of wavelet neural networks and genetic algorithm

An optimization method based on the combination of wavelet neural networks (WNN) and Pareto genetic algorithm was proposed, and was applied to the numerical optimization in internal flows. WNN is composed of input layer, hidden layer and output layer. It replaces the commonly used Sigmoid activation function in back propagation (BP) neural networks by Morlet wavelet generating functions in hidden layer. Pareto genetic algorithm has great global optimum ability and optimization efficiency. Generally, it can always gain uniformly-distributed Pareto optimal solution set. Typical algorithm examples indicate that this algorithm can complete approaching and mapping of non-linear function quickly, efficiently and accurately, with great generalization ability.