Abstract
Based on Clonal Selection Theory, an adaptive Parallel Immune Evolutionary Strategy (PIES) is presented. On the grounds of antigen-antibody affinity, the original antibody population can be divided into two subgroups. Correspondingly, two operators, Elitist Clonal Operator (ECO) and Super Mutation Operator (SMO), are proposed. The former is adopted to improve the local search ability while the latter is used to maintain the population diversity. Thus, population evolution can be actualized by concurrently operating ECO and SMO, which can enhance searching efficiency of the algorithm. Experimental results show that PIES is of high efficiency and can effectively prevent premature convergence. Therefore, it can be employed to solve complicated optimization problems.
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References
Iiu, R., Du, H., Jiao, L.: Immunity Clonal Strategy. In: Fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2003), pp. 290–295 (2003)
Watkins, A., Timmis, J.: Exploiting Parallelism Inherent in AIRS, an Artificial Immune Classifier. In: Nicosia, G., Cutello, V., Bentley, P. (eds.) The 3rd International Conference on Artificial Immune Systems, pp. 427–438. Springer-Verlag, Berlin, Heidel berg (2004)
KongYu, Y., XiuFeng, W.: Research and Implement of Adaptive Multimodal Immune Evolution Algorithm. Control and Decision 20(6), 717–720 (2005)
De Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)
Ada, G.L., Nossal, G.: The Clonal Selection Theory. Scientific American 257(2), 50–57 (1987)
Xiangjun, W., Dou, J., Min, Z.: A Multi-Subgroup Competition Evolutionary Programming Algorithm. Acta Electronica Sinica 11(32), 1824–1828 (2004)
Yin-sheng, L., Ren-hou, L., Weixi, Z.: Multi-modal Functions Parallel Optimization Algorithm Based on Immune Mechanism. Journal of System Simulation 2(11), 319–322 (2005)
Chellapillal, K., Fogel, D.: Two New Mutation Operators for Enhanced Search and Optimization in Evolutionary Programming. In: Dikaiakos, M.D. (ed.) Applications of Soft Computing. SPIE. LNCS, vol. 3165, pp. 260–269. Springer, Heidelberg (2004)
Lavine, B.K. (ed.): Pattern Recognition Analysis via Genetic Algorithm & Multivariate Statistical Methods, vol. 315, pp. 145–148. CRC Press, Boca Raton Fla (2000)
Ruochen, L., Haifeng, D., Licheng, J.: An Immune Monoclonal Strategy Algorithm. Acta Electronica Sinica 11(32), 1880–1884 (2004)
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Bo, C., Zhenyu, G., Binggang, C., Junping, W. (2007). Adaptive Parallel Immune Evolutionary Strategy. In: Wang, Y., Cheung, Ym., Liu, H. (eds) Computational Intelligence and Security. CIS 2006. Lecture Notes in Computer Science(), vol 4456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74377-4_22
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DOI: https://doi.org/10.1007/978-3-540-74377-4_22
Publisher Name: Springer, Berlin, Heidelberg
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