文献类型：期刊文献

中文题名：Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation

英文题名：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)

作者：陶莉莉[1];孔祥东[1];钟伟民[2];钱锋[1]

机构：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes,Ministry of Education,East China University of Science and Technology;[2]Automation Institute,East China University of Science and Technology

年份：2012

卷号：20

期号：6

起止页码：1047

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2011_2012】；

基金：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)

语种：中文

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

外文关键词：自适应免疫遗传算法;非线性优化问题;二甲苯氧化;不良反应;燃烧;过早收敛;内存插槽;搜索能力

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.
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.