A Novel Immune Clonal Algorithm
This paper proposes a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QICA is also characterized by the representation of the antibody (individual), the evaluation function, and the population dynamics. However, in QICA, antibody is proliferated and divided into a set of subpopulation groups. Antibodies in a subpopulation group are represented by multi-state gene quantum bits. In the antibody’s updating, the scabilitable quantum rotation gate strategy and dynamic adjusting angle mechanism are applied to guide searching. Theoretical analysis has proved that QICA converges to the global optimum. Some simulations are given to illustrate its efficiency and better performance than its counterpart.
KeywordsClonal Selection Artificial Immune System Clonal Size Antibody Population Clonal Selection Theory
Unable to display preview. Download preview PDF.
- 1.De Castro, L.N., Von Zuben, F.J.: Artificial Immune Systems: Part II—A Survey of Applications. FEEC/Univ. Campinas, Campinas, Brazil (2000), [Online] Available: http://www.dca.fee.unicamp.br/~lnunes/immune.html
- 2.Moore, M., Narayanan, A.: Quantum-Inspired Computing. Dept. Comput. Sci., Univ. Exeter, Exeter, UK (1995)Google Scholar
- 3.Li, Y.Y., Jiao, L.C.: Quantum-Inspired Immune Clonal Algorithm. In: Jacob, C., Pilat, M.L., Bentley, P.J., et al. (eds.) Proceedings of the 4th International Conference on Artificial Immune Systems, Banff, Alberta, Canada, August 2005, pp. 304–317 (2005)Google Scholar
- 4.Burnet, F.M.: Clonal Selection and After. In: Bell, G.I., Perelson, A.S., Pimbley Jr., G.H. (eds.) Theoretical Immunology, pp. 63–85. Marcel Dekker Inc., New York (1978)Google Scholar
- 5.Du, H.F., Jiao, L.C., Wang, S.A.: Clonal Operator and Antibody Clone Algorithms. In: Shichao, Z., Qiang, Y., Chengqi, Z. (eds.) Proceedings of the First International Conference on Machine Learning and Cybernetics, pp. 506–510. IEEE, Beijing (2002)Google Scholar