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改进的自适应免疫算法及其在对二甲苯氧化燃烧副反应优化中的应用(英文)
引用本文:陶莉莉,孔祥东,钟伟民,钱锋.改进的自适应免疫算法及其在对二甲苯氧化燃烧副反应优化中的应用(英文)[J].中国化学工程学报,2012,20(6):1047-1052.
作者姓名:陶莉莉  孔祥东  钟伟民  钱锋
作者单位: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
基金项目: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: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  
收稿时间:2012-04-16

Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation*
TAO Lili,KONG Xiangdong,ZHONG Weimin, and QIAN Feng.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.
Authors:TAO Lili  KONG Xiangdong  ZHONG Weimin  and QIAN Feng
Affiliation: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
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.
Keywords:
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