| 一种改进的混合遗传算法及在化工过程动态优化中的应用(英文) |
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| 引用本文： | 孙帆,杜文莉,祁荣宾,钱锋,钟伟民. 一种改进的混合遗传算法及在化工过程动态优化中的应用(英文)[J]. 中国化学工程学报, 2013, 21(2): 144-154. DOI: 10.1016/S1004-9541(13)60452-8 |
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| 作者姓名： | 孙帆 杜文莉 祁荣宾 钱锋 钟伟民 |
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| 作者单位： | Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China |
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| 基金项目： | Supported by Major State Basic Research Development Program of China(2012CB720500);National Natural Science Foundation of China(Key Program:U1162202);National Natural Science Foundation of China(21276078, 21206037);National Science Fund for Outstanding Young Scholars(61222303);the Fundamental Research Funds for the Central Universities |
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| 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.
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| 关 键 词： | genetic algorithm simplex method dynamic optimization chemical process |
| 收稿时间： | 2011-03-31 |
A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes |
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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]. Chinese Journal of Chemical Engineering, 2013, 21(2): 144-154. DOI: 10.1016/S1004-9541(13)60452-8 |
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| Authors: | SUN Fan DU Wenli QI Rongbin QIAN Feng ZHONG Weimin |
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| Affiliation: | Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China |
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| 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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| Keywords: | genetic algorithm simplex method dynamic optimization chemical process |
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