Design of fixed and ladder mutation factor-based clonal selection algorithm for solving unimodal and multimodal functions

Authors:
Suresh Chittineni;A. N. S. Pradeep;Dinesh Godavarthi;Suresh Chandra Satapathy;S. Mohan Krishna;P. V. G. D. Prasad Reddy
Affiliations:
Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India;Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India;Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India;Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, India;Gitam University, Visakhapatnam, India;Andhra University Engineering College, Visakhapatnam, Andhra Pradesh, India
Venue:
Applied Computational Intelligence and Soft Computing
Year:
2011

Citing 8
Cited 0

Artificial Immune Systems: A New Computational Intelligence Paradigm

Artificial Immune Systems: A New Computational Intelligence Paradigm
An Improved Immune Evolutionary Algorithm for Multimodal Function Optimization

ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
JADE: adaptive differential evolution with optional external archive

IEEE Transactions on Evolutionary Computation
A Clone Selection Algorithm for the Open Vehicle Routing Problem

WGEC '09 Proceedings of the 2009 Third International Conference on Genetic and Evolutionary Computing
Handling constraints in global optimization using an artificial immune system

ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle

IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions

IEEE Transactions on Evolutionary Computation
Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.03

Visualization

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 proposedmethods laddermutation-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.