Design of Fixed and Ladder Mutation Factor‐Based Clonal Selection Algorithm for Solving Unimodal and Multimodal Functions

Abstract

Clonal selection algorithms (CSAs) is a special class of immune algorithms (IA), inspired by the clonal selection principle of the human immune system. To improve the algorithm's ability to perform better, this CSA has been modified by implementing two new concepts called fixed mutation factor and ladder mutation factor. Fixed mutation factor maintains a constant factor throughout the process, where as ladder mutation factor changes adaptively based on the affinity of antibodies. This paper compared the conventional CLONALG, with the two proposed approaches and tested on several standard benchmark functions. Experimental results empirically show that the proposed methods ladder mutation-based clonal selection algorithm (LMCSA) and fixed mutation clonal selection algorithm (FMCSA) significantly outperform the existing CLONALG method in terms of quality of the solution, convergence speed, and solution stability.

A new clonal selection algorithm based on radius regularization of anomaly detectors

“…By reviewing the researches in clonal selection field, we can taxonomy them in 3 categories; (i) the researches that consider the main aspects of operation algorithm: Cloning, mutation and selection [15,16], (ii) the researches that Regulate parameters to achieve high accuracy [1,14,2] and (iii) the researches that use clonal selection to optimize the proposed algorithms or their parameters [3,4,8,9,10].…”

“…The Mutation helps antibodies to mutate and improve their fitness. Uniform mutation, Gaussian Mutation or Cauchy mutation can be used as mutation function [1].…”

“…Cloning is one of the key aspects in AIS. The number of clones for each detector can be calculated by equation (5) that defined in [1]: (5) Where Nc is the Number of clones generated from each simulated cell; N is The size of antibody pool (detector), n is the number of antibodies with highest affinities and is a multiplying factor. Round (.)…”

“…Clone size of each selected detector is represented by each term of this sum. The numbers of clones that are generated for detectors with higher fitness, which is placed on top of the sorted fitness array, are more than bottom detectors [1].…”