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. Keywords self-adaptive immune genetic algorithm, artificial neural network, measurement, p-xylene oxidation process

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“…It is an intelligent search method that emerged by drawing on the natural selection of biological nature and the law of genetic biological evolution [ 8 ]. Its genetic characteristics are in line with the memory requirements of the time series characteristics of gas emission data, and the characteristics of gas emission in the next period can be inferred from the characteristics of gas emission quantity in the previous period [ 9 ]. The algorithm generates a random initial solution, and through repeated selection, crossover, and mutation, the global optimal solution is obtained.…”