一种换热器网络优化新方法

Optimization of heat exchanger networks based on multi-layer-perceptron

基于遗传算法和神经网络的多层感知器模型的有机结合,提出一种优化换热器网络的新算法和一种新的编码方法——基因矩阵. 这种算法根据遗传适应度(目标函数)的大小,以随机搜索方式寻找在求解区域的最优解,采用神经网络多层感知器模型实现换热器网络的结构优化和参数优化. 经过遗传-感知模型优化并与外逼近算法做了比较,表明采用此法优化多维、多峰、非凸的换热器网络也具有很好的适应性.

An optimization algorithm for heat exchanger networks and a new kind of gene matrix are proposed in the paper. Objective function is established as fitness of genetic algorithm and the weighted values are used to represent the structure of heat exchanger networks. Due to that with the genetic algorithm the optimal solution can be searched randomly at the whole solution space, multi-layer-perceptron trained by genetic algorithm is used to derive the best structure and parameters in the evolutionary computation. According to the results, the genetic-perceptron model is proved to be helpful to solve multi-peak nonconvex problem and it is efficient to optimize heatexchanger networks by genetic-perceptron model.