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.
Index Terms—clonal selection, antibody, antigen, affinity maturation, mutation.
David F. W. Yap is with the Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Hang TuahJaya,76100 Durian Tunggal, Melaka, Malaysia (phone: 606-555-2094; fax:606-555-2112; e-mail: firstname.lastname@example.org).
S. P. Koh and S.K.Tiong are with the College of Engineering, Universiti Tenaga Nasional (UNITEN), Km 7, Kajang-Puchong Road, 43009 Kajang,Selangor Darul Ehsan, Malaysia.
Cite: David F. W. Yap, S. P. Koh, S.K.Tiong, "Mathematical Function Optimization using AIS Antibody Remainder method,"International Journal of Machine Learning and Computing vol. 1, no. 1, pp. 13-19, 2011.