Black Box System Multi-Objective Optimization Based on Design of Experiment

In this paper, a multi-objective parameter optimization model based on experimental design and NN-GA is established. In this method, utilizing experimental design principle to deal with test project and applying NN to map and using Pareto genetic algorithm to optimize, multi-objective parameter optimization is accomplished, in which the high nonlinear mapping ability of neural network model, the global research ability of genetic algorithms and multiform choice about the test points according to experimental demand are utilized synthetically. A Pareto-optimal set can be found in specify region. The method can be applied broadly and it needn't the concrete mathematic model for different optimizing demand. For virtual devices and products, the virtual experiments can be realized by parameter-driven characteristic.