Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of <i>p</i>-Xylene Oxidation<sup>*</sup>

陶莉莉; 孔祥东; 钟伟民; 钱锋

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

Updated：2026-01-06

- 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^*
- 中国化学工程学报（英文版） 2012年20卷第6期页码：1047-1052
- Affiliations：
  
  1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology,Shanghai,China,200237
  2. Automation Institute, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
  
  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)
- DOI：
  中图分类号：
- 纸质出版：2012
- Accepted：
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陶莉莉, 孔祥东, 钟伟民, 等. Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation^*[J]. 中国化学工程学报（英文版）, 2012,20(6):1047-1052.

-Xylene Oxidation
陶莉莉, 孔祥东, 钟伟民, 等. Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation^*[J]. 中国化学工程学报（英文版）, 2012,20(6):1047-1052. DOI：

-Xylene Oxidation DOI：

摘要

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

-xylene oxidation

and satisfactory results are obtained.

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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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摘要

Abstract

关键词

Keywords

references

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^*