基于GA-NN的复杂工艺生产过程多目标优化研究

在复杂非线性多目标优化问题求解中,非线性模型结构很难事先给定,需要检验的参数也非常繁多,应用传统的建模方法和优化模型已难以解决更为复杂的现实问题。人工神经网络技术为解决复杂非线性系统建模问题提供了一条新的途径。将神经网络响应面作为目标函数或者约束条件,加上其他常规约束条件进行系统模型的建立,再应用遗传算法进行优化,从而实现设计分析与设计优化的分离。以某化工企业的生产过程优化问题为例,利用BP神经网络建立了工艺参数与性能目标之间的模型,然后利用遗传算法搜索最优工艺参数,获取了用于指导生产的样本点数据。研究结果表明,该方法能够获得高精度的多目标优化模型,从而使优化效率大为提高。

Multi-objective Optimization Design of Complex Production Processing Based on Genetic Algorithm and Neural Network

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.