Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation*

Chinese Journal of Chemical Engineering ›› 2012, Vol. 20 ›› Issue (6): 1047-1052.

Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation^*

陶莉莉¹, 孔祥东¹, 钟伟民², 钱锋¹

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
2. Automation Institute, East China University of Science and Technology, Shanghai 200237, China

收稿日期:2012-04-16 修回日期:2012-07-10 出版日期:2012-12-28 发布日期:2012-12-28
通讯作者: ZHONG Weimin,E-mail:wmzhong@ecust.edu.cn;QIAN Feng,E-mail:fqian@ecust.edu.cn
基金资助:
Supported by the Major State Basic Research Development Program of China (2012CB720500);the National Natural Science Foundation of China (Key Program: U1162202);the National Natural Science Foundation of China (General Program:61174118);Shanghai Leading Academic Discipline Project (B504)

Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation^*

TAO Lili¹, KONG Xiangdong¹, ZHONG Weimin², QIAN Feng¹

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
2. Automation Institute, East China University of Science and Technology, Shanghai 200237, China

Received:2012-04-16 Revised:2012-07-10 Online:2012-12-28 Published:2012-12-28
Supported by:
Supported by the Major State Basic Research Development Program of China (2012CB720500);the National Natural Science Foundation of China (Key Program: U1162202);the National Natural Science Foundation of China (General Program:61174118);Shanghai Leading Academic Discipline Project (B504)

摘要/Abstract

摘要： In recent years,immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications.However,IGA with deterministic mutation factor suffers from the problem of premature convergence.In this study,a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases,in which immune concepts are applied to determine the mutation parameters,is proposed to improve the searching ability of the algorithm and maintain population diversity.Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem.This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation,and satisfactory results are obtained.

关键词: self-adaptive immune genetic algorithm, artificial neural network, measurement, p-xylene oxidation process

Abstract: In recent years,immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications.However,IGA with deterministic mutation factor suffers from the problem of premature convergence.In this study,a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases,in which immune concepts are applied to determine the mutation parameters,is proposed to improve the searching ability of the algorithm and maintain population diversity.Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem.This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation,and satisfactory results are obtained.

Key words: self-adaptive immune genetic algorithm, artificial neural network, measurement, p-xylene oxidation process

陶莉莉, 孔祥东, 钟伟民, 钱锋. Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1047-1052.

TAO Lili, KONG Xiangdong, ZHONG Weimin, QIAN Feng. Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation^*[J]. Chin.J.Chem.Eng., 2012, 20(6): 1047-1052.

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Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation^*

Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation^*

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