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

Mohammed Abo-Zahhad, Sabah M. Ahmed, Nabil Sabor, Ahmad F. Al-Ajlouni

Communication Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid, Jordan.
Electrical and Electronics Engineering Department, Faculty of Engineering, Assiut University, Assiut, Egypt.

DOI: 10.4236/jsip.2012.31011                

Abstract

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 (p_m) is adaptively varied depending on the fitness values of the solutions. 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 p_m 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 function; 2) a rapidly varying multimodal function and 3) design of a second order 2-D narrow band recursive LPF. Simulation results show that the proposed method efficiently improves IA’s performance and prevents it from getting stuck at a local optimum.

Keywords

Adaptive Mutation; Immune Algorithm; Convergence; Traditional Mutation

Conflicts of Interest

The authors declare no conflicts of interest.

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