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面向多峰值函数优化的人工免疫网络算法特性分析 被引量：2

Algorithm Analysis of the Artificial Immune Network for Multimodal Function Optimization
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摘要 对用于多峰值函数的人工免疫网络算法进行了改进并分析了其特性。首先在给出基于人工免疫网络的多峰值函数优化算法及其流程的基础上,提出了一种克服早熟现象的改进方案;然后通过与克隆选择算法的数值对比实验,对改进后算法的计算结果加以分析,验证了该算法用于求解多峰值函数优化的有效性;最后重点讨论了算法主要参数对其求解性能的影响,得到了若干参考性结论,可为人工免疫网络计算提供指导。 An improved artificial immune network algorithm for multimodal function optimization along with its characteristic analysis is presented in this paper. Firstly, an artificial immune network algorithm for multimodal function optimization (opt-aiNet) as well as its implementation process is introduced. Based on that we proposed a revised version to overcome the premature phenomenon. Then performance of the improved algorithm is compared with CLONALG, a typical clonal selection algorithm, through some numerical experiments, and its advantages as well as the effectiveness in multimodal function optimization are verified. Finally, the influence of parameters on performance of the improved algorithm is discussed in detail and some valuable conclusions are reached, which can be used to guide artificial immune network computing.
作者 肖人彬 刘勇 窦刚
机构地区 华中科技大学CAD中心
出处 《模式识别与人工智能》 EI CSCD 北大核心 2005年第1期17-24,共8页 Pattern Recognition and Artificial Intelligence
基金 高等学校博士点基金(No.20030487054)
关键词 多峰值函数优化 人工免疫网络 克隆选择 Opt-aiNet算法 Multimodal Function Optimization Artificial Immune Network Clonal Selection Opt--aiNet Algorithm
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献13

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共引文献249

同被引文献19

  • 1钟将,吴中福,吴开贵,欧灵.基于人工免疫网络的动态聚类算法[J].电子学报,2004,32(8):1268-1272. 被引量：24
  • 2肖人彬.基于免疫计算的机构轨迹综合[J].计算机辅助设计与图形学学报,2004,16(6):812-818. 被引量：7
  • 3刘勇,肖人彬.机构轨迹生成理论研究进展[J].计算机辅助设计与图形学学报,2005,17(4):627-636. 被引量：15
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  • 7GOLDBERG D E, RICHARDSON J. Genetic algorithms with sharing for multimodal function optimization[ C]//Proc of the 2nd Interna- tional Conference on Genetic Algorithms and Their Application. Hills- dale, NJ : L. Erlhaum Associates Inc. , 1987:41-49.
  • 8BEASLEY D, BULL D R, MARTIN R R. A sequential niche tech- nique for multimodal function optimization [ J]. Evolution Computa- tion.1993.1 (2) ,101-125.
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  • 10De CASTRO L N, Von ZUBEN F J. Learning and optimization using the clonal selection principle [ J]. IEEE Trans on Evolutionary Computation, 2002,6 ( 3 ) : 239 - 251.

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模式识别与人工智能
2005年 第1期

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