摘要
现有人工蜂群算法的收敛性分析多是基于整体收敛性的分析方法,这些收敛性分析无法展现出人工蜂群算法在收敛过程中的收敛变化。文中采用数形结合的方式,结合目标函数图像,用阶段性分析的方法大致把蜂群算法的收敛过程分为全局搜索阶段和最优区域搜索阶段,利用人工蜂群算法在转移时需遵循一定程度上的平均分布的特征,逐步分析每个阶段的收敛过程和变化,最终得出人工蜂群算法的收敛结果和收敛特征。该方法可以清晰地展现出人工蜂群算法的收敛优势和缺陷以及算法收敛概率的变化过程。
The convergence analysis of existing methods for artificial bee colony algorithm(ABC)is based on the analysis method of global convergence.But these convergence analysis methods can’t show the convergence change in the convergence process of ABC.Firstly,the method of combination of number and shape is adopted,and the objective function diagram is combined to divide the convergence process of ABC into the global search stage and the optimal region search stage by using stage analysis.Then,the convergence process and changes of each stage are analyzed one by one based on transferring character that the artificial bees follow a certain degree of average distribution.Finally,the convergence results and change of ABC are obtained.This method can clearly show the convergence advantages and defects of the ABC algorithm,and reveal the changing process of the convergence probability of the algorithm.
作者
火久元
王野
胡卓娅
HUO Jiu-yuan;WANG Ye;HU Zhuo-ya(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)
出处
《计算机科学》
CSCD
北大核心
2018年第10期212-216,共5页
Computer Science
基金
国家自然科学基金项目(61462058)资助
关键词
人工蜂群算法
马尔可夫链
全局收敛
数形结合
Artificial bee colony algorithm
Markov chain
Global convergence
Combination of number and shape
