Clonal Selection Algorithm with Search Space Expansion Scheme for Global Function Optimization
Unlike evaluation strategy (ES) and evaluation programming (EP), clonal selection algorithm (CSA) strongly depends on the given search space for the optimal solution problem. The interval of existing optimal solution is unknown in most practical problem, then the suitable search space can not be given and the performance of CSA are influence greatly. In this study, a self-adaptive search space expansion scheme and the clonal selection algorithm are integrated to form a new algorithm, Self Adaptive Clonal Selection Algorithm, termed as SACSA. It is proved that SACSA converges to global optimum with probability 1.Qualitative analyzes and experiments show that, compared with the standard genetic algorithm using the same search space expansion scheme, SACSA has a better performance in many aspects including the convergence speed, the solution precision and the stability. Then, we study more about the new algorithm on optimizing the time-variable function. SACSA has been confirmed that it is competent for solving global function optimization problems which the initial search space is unknown.
Unable to display preview. Download preview PDF.
- 1.Zhong, W.C., Liu, J., Xue, M.Z., Jiao, L.C.: A Multiagent Genetic Algorithm for Global Numerical Optimization [J]. IEEE Trans. on Syst., Man, Cybern. A 34(2), 1136 (2004)Google Scholar
- 4.Cooper, K.D., Hall, M.W., Kennedy, K.: Procedure cloning[C]. In: Proceedings of the 1992 International Conference on Computer Languages, pp. 96–105 (1992)Google Scholar
- 5.Zhang, Z.K., Chen, H.C.: Stochastic Process (in Chinese), pp. 140–193. Xidian University Press, Xi’an (2003)Google Scholar
- 6.Swinburne, R.: Bayes’s Theorem. Oxford University Press, Oxford (2002)Google Scholar
- 7.Du, H.F., Jiao, L.C., Liu, R.C.: Adaptive Polyclonal Programming Algorithm with Applications. In: Proceedings of the Fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2003), IEEE, Los Alamitos (2003)Google Scholar
- 8.Du, H.F., Jiao, L.C., Gong, M.G., Liu, R.C.: Adaptive Dynamic Clone Selection Algorithms. In: Zdzislaw, P., Lotfi, Z. (eds.) Proceedings of the Fourth International Conference on Rough Sets and Current Trends in Computing, Uppsala, Sweden, pp. 768–773 (2004)Google Scholar
- 9.Lydyard, P.M., Whelan, A., Fanger, M.W.: Instant notes in immunology. BIOS Scientific Publishers Limited, Beijing (2000)Google Scholar
- 10.Balazinska, M., Merlo, E., Dagenais, M., et al.: Advanced clone-analysis to support object-oriented system refactoring [C]. In: Proceedings Seventh Working Conference on Reverse Engineering, pp. 98–107 (2000)Google Scholar
- 11.Smaili, N.E., Sammut, C., Shirazi, G.M.: Behavioural cloning in control of a dynamic system[C]. In: IEEE International Conference on Systems, Man and Cybernetics Intelligent Systems for the 21st Century, March 1995, pp. 2904–2909 (1995)Google Scholar
- 12.Hybinette, M., Fujimoto, R.: Cloning: A Novel Method for Interactive Parallel Simulation[C]. In: Proceedings of the 1997 Winter Simulation Conference, pp. 444–451 (1997)Google Scholar