doi: 10.7763/IJMLC.2011.V1.3
Mathematical Function Optimization using AIS Antibody Remainder method
David F. W. Yap1
, ,
- 1Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Hang TuahJaya,76100 Durian Tunggal, Melaka, Malaysia.
- 2College of Engineering, Universiti Tenaga Nasional (UNITEN), Km 7, Kajang-Puchong Road, 43009 Kajang,Selangor Darul Ehsan, Malaysia.
Abstract
Artificial immune system (AIS) is one of the metaheuristics used for solving combinatorial optimization problems. In AIS, clonal selection algorithm (CSA) has good global searching capability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution.Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. The results show that the proposed algorithm is able to improve the conventional CSA interms of accuracy and stability for single and multi objective functions.
Keywords
- clonal selection
- antibody
- antigen
- affinity maturation
- mutation
How to Cite
David F. W. Yap, S. P. Koh, and S.K.Tiong, "Mathematical Function Optimization using AIS Antibody Remainder method," International Journal of Machine Learning, vol. 1, no. 1, pp. 13-19, 2011. https://doi.org/10.7763/IJMLC.2011.V1.3
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).