改进克隆选择算法的收敛性分析  

Convergence analysis of improved clonal selection algorithm

作　　者：郑仙花[1] 骆炎民[1] 

机构地区：[1]华侨大学计算机科学与技术学院,福建厦门361021

出　　处：《计算机应用》2013年第3期810-813,共4页journal of Computer Applications

基　　金：福建省自然科学基金资助项目(2012J01273);泉州市科技计划项目(2010Z53)

摘　　要：为了完善克隆选择算法(CSA),使算法理论上成熟,利用两个随机收敛性度量:完全收敛和均值收敛,证明基于多类数据分类的改进克隆选择算法(Multi_CSA)满足收敛到全局最优解的充分条件,并以实验数据进行验证。从理论上证明了Multi_CSA满足收敛的充分条件,实验方面也表明该算法在经过一定的代数后会收敛。理论和实验上均表明:Multi_CSA是一个能在有限代内收敛的较为成熟算法。In order to improve Clonal Selection Algorithm （CSA） and make it theoretically mature, this paper adopted two random convergence measures： complete convergence and mean convergence to do the convergence analysis for the proposed algorithm named improved clonal Selection Algorithm for Multi-class Classification （Multi_CSA）. It demonstrated that the Multi_CAS satisfied the sufficient condition for convergence to a global optimal solution. An experiment was also performed to validate the result. The paper proves that Multi CAS meets the sufficient condition for convergence. The experiment shows that the algorithm will converge after several generations. It is concluded that Multi_CSA can converge within limited generation and it is a relatively mature algorithm.

关 键 词：人工免疫 克隆选择 分类 收敛性 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]