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

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.

[1]  Massimo Pisu,et al.  Kinetics of p‐xylene liquid‐phase catalytic oxidation , 1994 .

[2]  Vladimir Agabekov,et al.  Effect of catalyst composition on decreasing of CO2 and CO formation in synthesis of aromatic acids , 1995 .

[3]  Youn-Woo Lee,et al.  Reaction Pathway and Kinetics for Uncatalyzed Partial Oxidation of p-Xylene in Sub- and Supercritical Water , 2002 .

[4]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[5]  Jonathan Timmis,et al.  AINE: an immunological approach to data mining , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[6]  Stephanie Forrest,et al.  Architecture for an Artificial Immune System , 2000, Evolutionary Computation.

[7]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[8]  Kalmanje KrishnaKumar,et al.  Artificial Immune System Approaches for Aerospace Applications , 2003 .

[9]  Hajir Karimi,et al.  Correlation of Vapour Liquid Equilibria of Binary Mixtures Using Artificial Neural Networks , 2007 .

[10]  Jon Timmis,et al.  A Multi-Layered Immune Inspired Approach to Data Mining , 2003 .

[11]  W. Du,et al.  Development of a Free Radical Kinetic Model for Industrial Oxidation of p-Xylene Based on Artificial Neural Network and Adaptive Immune Genetic Algorithm , 2012 .

[12]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[13]  Vera Kurková,et al.  Kolmogorov's theorem and multilayer neural networks , 1992, Neural Networks.

[14]  J. Neidhoefer,et al.  Immunized Adaptive Critic for an Autonomous Aircraft Control Application , 1999 .

[15]  Hecht-Nielsen.  Theory of the backpropagation neural network , 1989 .

[16]  Randall S. Sexton,et al.  Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing , 1999, Eur. J. Oper. Res..

[17]  Dipankar Dasgupta,et al.  An immunogenetic approach in chemical spectrum recognition , 2003 .

[18]  Peter J. Bentley,et al.  Immune Memory in the Dynamic Clonal Selection Algorithm , 2002 .

[19]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Andrew M. Tyrrell,et al.  Immunotronics: Hardware Fault Tolerance Inspired by the Immune System , 2000, ICES.

[21]  Qian Feng,et al.  Operation Condition Optimization of p-Xylene Oxidation Reaction Process Based on a Fuzzy Adaptive Immune Algorithm , 2010 .