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


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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.



Edited by:

L. Gao, W.D. Li, Y.X. Zhao and X.Y. Li






T. Z. Sui et al., "Black Box System Multi-Objective Optimization Based on Design of Experiment", Advanced Materials Research, Vol. 544, pp. 12-17, 2012

Online since:

June 2012




[1] Z.H. Du, X.G. Wang, and C.C. Di, An Optimal Design Based on Experiment, Neural Networks and Genetic Algorithm, Mechanical Design and Manufacture, Vol. 30 (2001), pp.22-26.

[2] K.T. Fang and C. X Ma, Orthogonal and Uniform Experimental Design, Beijing Science Press, Beijing, (2001).

[3] N. Srinivas and D. Kalyanmoy, Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation, Vol. 2 (1994), pp.221-248.

DOI: 10.1162/evco.1994.2.3.221

[4] C.M. Fonseca and P.J. Fleming, An overview of evolutionary algorithms in multi-objective optimization, Evolutionary Computation, Vol. 3 (1995), pp.1-16.

[5] Y.W. Leung and Y.P. Wang, Multiobjective programming using uniform design and genetic algorithm, IEEE Trans Systems, Man and Cybernetics, Part C, Vol. 30 (2000), pp.293-304.

DOI: 10.1109/5326.885111

[6] S. Kneer, L. Personnaz, and G. Dreyfus, Handwritten digit recognition by neural networks with single-layer training, IEEE Trans on Neural Networks, Vol. 3 (1992), pp.926-968.

DOI: 10.1109/72.165597

[7] X.P. Wang and L.M. Cao, Genetic Algorithms-theory, application and software realization, Xian University Press, Xian, (2002).

[8] X.P. Wang, Pareto genetic algorithm for multi-objective optimization design, Systems Engineering and Electronics, Vol. 25 (2003), pp.1558-1561.

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