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

doi:10.1016/S1004-9541(13)60452-8

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

过程系统工程与过程安全 | Updated：2026-01-06

- A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes
- Chinese Journal of Chemical Engineering Vol. 21, Issue 2, Pages: 144-1541004-954160452-8(2013)
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(13)60452-8
  CLC：
- Published：2013
- Accepted：
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SUN Fan, DU Wenli, QI Rongbin, QIAN Feng, ZHONG Weimin. A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes[J]. Chin.J.Chem.Eng., 2013, 21(2): 144-154.
DOI：

SUN Fan, DU Wenli, QI Rongbin, QIAN Feng, ZHONG Weimin. A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes[J]. Chin.J.Chem.Eng., 2013, 21(2): 144-154. DOI： 10.1016/S1004-9541(13)60452-8.

摘要

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.

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