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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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
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