Optimization and Quality Factor of Clonal Selection Algorithm
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National Natural Science Foundation of China (61603420); National Natural Science Foundation of Hubei Province (2014CFB413); Special Fund for Basic Scientific Research of Central Colleges, South-Central University for Nationalities (CZY14007); Scientific Computing and Intelligent Information Processing of Guangxi University Key Laboratory of Science Open Fund (GXSCIIP201412)

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    Abstract:

    To tackle the problem that traditional clonal selection algorithm may suffer from premature convergence phenomenon and is lack of crossover operator problems, this paper proposes a new efficient clonal annealing optimization algorithm. The proposed algorithm combines simulated annealing algorithm with clonal selection mechanism of immune system, and maintains the balance of global and local search. The algorithm can effectively improve search efficiency, so as to speed up the convergence rate. Meanwhile, a quality factor model is used to analyze the dynamic performance of the algorithm, and an analysis of its convergence is performed using Markov chain theory. Finally, the proposed algorithm is applied to the association rule data mining, achieving satisfactory results.

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舒万能,丁立新.克隆选择算法的优化和品质因数.软件学报,2016,27(11):2763-2776

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History
  • Received:April 09,2013
  • Revised:July 09,2014
  • Adopted:
  • Online: November 04,2015
  • Published:
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