SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2013, Vol. 21 ›› Issue (2): 144-154.DOI: 10.1016/S1004-9541(13)60452-8

• 过程系统工程与过程安全 • 上一篇    下一篇

A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes

孙帆, 杜文莉, 祁荣宾, 钱锋, 钟伟民   

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
  • 收稿日期:2011-03-31 修回日期:2012-09-21 出版日期:2013-02-28 发布日期:2013-03-13
  • 通讯作者: QIAN Feng
  • 基金资助:

    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.

A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes

SUN Fan, DU Wenli, QI Rongbin, QIAN Feng, ZHONG Weimin   

  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
  • Received:2011-03-31 Revised:2012-09-21 Online:2013-02-28 Published:2013-03-13

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.

关键词: genetic algorithm, simplex method, dynamic optimization, chemical process

Abstract: 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.

Key words: genetic algorithm, simplex method, dynamic optimization, chemical process