| 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: | |
| 本文献已被 CNKI ScienceDirect 等数据库收录！ |
| 点击此处可从《中国化学工程学报》浏览原始摘要信息 |
|
点击此处可从《中国化学工程学报》下载全文 |
|
