摘要
人工蜂群算法(ABC)是一种模拟蜜蜂群智能搜索行为的随机优化算法,已成功用于解决许多优化问题.为有效改善ABC算法的性能,文章结合思维进化的思想提出了一种思维进化蜂群算法(MEABC),该算法通过学习和按维更新策略对ABC算法进行了改进,并对改进算法的收敛性进行了分析.通过四个标准测试函数的仿真实验,验证了MEABC算法能有效避免早熟收敛,全局优化能力和收敛速率都有显著提高.
Artificial bee colony （ABC） algorithm is a new global stochastic optimization algorithm, which mimics the intelli- gent behavior of honeybee swamis. It has been used to solve various optimization problems successfully. In order to further improve the performance of artificial bee colony algorithm, a mind evolutionary aaificial bee colony algorithm （MEABC） based on the idea of mind evolutionary is proposed. Two strategies based on opposition learning and dimension updating are applied to MEABC algorithm, and the convergence of the MEABC algorithm is analyzed. Experimental results on four benchmark functions show that the MEABC algorithm can effectively avoid the premature convergence, greatly enhance the global optimization ability and improve the convergence speed.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2015年第5期948-955,共8页
Acta Electronica Sinica
关键词
全局优化
人工蜂群算法
思维进化算法
收敛性
global optimization
artificial bee colony
mind evolutionary algorithm
convergence
