文献类型：期刊文献

英文题名：A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes

作者：Sun Fan[1];Du Wenli[1];Qi Rongbin[1];Qian Feng[1];Zhong Weimin[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2013

卷号：21

期号：2

起止页码：144

外文期刊名：CHINESE JOURNAL OF CHEMICAL ENGINEERING

收录：;EI(收录号：20131316154693);WOS:【SCI-EXPANDED(收录号:WOS:000318887700005)】；

基金：Supported by Major State Basic Research Development Program of China (2012CB720500), National Natural Science Foundation of China (Key Program: U1162202), National Science Fund for Outstanding Young Scholars (61222303), National Natural Science Foundation of China (21276078, 21206037) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：genetic algorithm; simplex method; dynamic optimization; chemical process

摘要：The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature. Genetic algorithm (GA) has been proved to be a feasible method when the gradient is difficult to calculate. Its advantage is that the control profiles at all time stages are optimized simultaneously, but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum. In this study, a hybrid improved genetic algorithm (HIGA) for solving dynamic optimization problems is proposed to overcome these defects. Simplex method (SM) is used to perform the local search in the neighborhood of the optimal solution. By using SM, the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved. The hybrid algorithm presents some improvements, such as protecting the best individual, accepting immigrations, as well as employing adaptive crossover and Gaussian mutation operators. The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems. At last, HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.