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"A New Method for Fastening the Convergence of Immune Algorithms Using an Adaptive Mutation Approach"

مؤلف البحث
Mohammed Abo-Zahhad, Sabah M. Ahmed, Nabil Sabor and Ahmad F. Al-Ajlouni
المشارك في البحث
سنة البحث
2012
مجلة البحث
Journal of Signal and Information Processing
الناشر
NULL
عدد البحث
Vol.3
تصنيف البحث
1
صفحات البحث
PP.86-91
موقع البحث
w.scirp.org/journal/PaperInformation.aspx?PaperID=17674#.VY6GtbWDxMM
ملخص البحث

This paper presents a new adaptive mutation approach for fastening the convergence of immune algorithms (IAs). This method is adopted to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the IA. In this method, the mutation rate (pm) is adaptively varied depending on the fitness values of the so-lutions. Solutions of high fitness are protected, while solutions with sub-average fitness are totally disrupted. A solution to the problem of deciding the optimal value of pm is obtained. Experiments are carried out to compare the proposed approach to traditional one on a set of optimization problems. These are namely: 1) an exponential multi-variable func-tion; 2) a rapidly varying multimodal function and 3) design of a second order 2-D narrow band recursive LPF. Simula-tion results show that the proposed method efficiently improves IA’s performance and prevents it from getting stuck at a local optimum.