Modified Self-adaptive Immune Genetic Algorithm for Optimization in the Combustion Side Reaction of p-Xylene Oxidation
In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution of non-linear optimization problems encountered in many engineering applications. In IGA, the mutation factor values are either fixed or change together according to a function of the individual’s current generation number during all the search process. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. 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 strengthen 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 can quickly converge to the global optimum and overcome premature problem. Then, this algorithm is applied to optimize a feed forward neural network to measure the content of products in the combust ion side reaction of p-xylene oxidation, and satisfactory results are obtained.
Self-adaptive immune genetic algorithm Artificial neural network Measurement p-Xylene oxidation process
Lili Tao Xiangdong Kong Weimin Zhong Feng Qian
Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, E Automation Institute, East China University of Science and Technology, Shanghai 200237, China Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, E
国内会议
厦门
英文
1-6
2012-08-01（万方平台首次上网日期，不代表论文的发表时间）