Abstract: In the problem solving processing of complex non-linear multi-objective optimization, it is very difficult to getting the non-linear structure model beforehand and the considered parameters become more and more. I}he conventional modeling method and optimal model have many shortcomings, and arc difficult to solve currently complicated engineering practical problems. Artificial neural network provides a novel approach for solving the complex nonlinear system modeling problems. The trained neural network response surfaces can either be objective function or constraint con- ditions, and together with other conventional constraints, a system model is then set up and it can be optimized by genetic algorithm. This allows the separation between design analysis modeling and optimization searching. Through an example of the production process optimization problem of a chemical enterprise,the model of process parameters and performance target based on Backward Propagation neural network response surface was constructed, and the optimal process parameters and sample data were gained by genetic algorithm. The experiment results illustrate that the proposed method can get multi objective optimal model with Mgh accuracy, thus greatly raising the efficiency of optimization process.

Key words: Neural network, Genetic algorithm, Multi objective optimization, Modeling