Black Box System Multi-Objective Optimization Based on Design of Experiment
| Periodical | Advanced Materials Research (Volume 544) |
|---|---|
| Main Theme | Advances in Product Development and Reliability III |
| Edited by | L. Gao, W.D. Li, Y.X. Zhao and X.Y. Li |
| Pages | 12-17 |
| DOI | 10.4028/www.scientific.net/AMR.544.12 |
| Citation | Tian Zhong Sui et al., 2012, Advanced Materials Research, 544, 12 |
| Online since | June, 2012 |
| Authors | Tian Zhong Sui, Lei Wang, Dong Mei Cheng, Hong Wen Cui |
| Keywords | Black Box System, Experimental Design, Genetic Algorithm (GA), Multi-Objective Optimization, Neural Network (NN) |
| Price | US$ 28,- |
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.
