摘要
人工蜂群(Artificial Bee Colony,ABC)算法在求解函数最优值时,存在后期收敛速度慢、易于陷入局部最优、疏于开发等问题.为了解决这些问题,对算法进行了深入研究,结合其他仿生智能优化算法的机制,提出了一种能有效提高收敛速度,增强算法开发性和全局寻优能力,并能有效避免种群个体陷入局部最优的算法——基于交叉的全局人工蜂群算法.选取7个标准测试函数进行实验仿真,结果表明,与ABC算法、全局最优人工蜂群算法(GABC)相比,基于交叉的全局人工蜂群算法(CGABC)的收敛速度及精度均有明显提高.
The shortcomings of artificial bee colony algorithm include slow convergence speed,easily falling into local optimum value,neglect of development and other issues.In order to overcome these problems,referencing the mechanism of other bionic intelligent optimization algorithms,a new algorithm of global artificial bee colony algorithm based on crossover is proposed,which can effectively improve the convergence rate,enhance the development of the algorithm and the global optimization ability,and the algorithm can effectively avoid the local optimum.Finally,seven standard test functions are selected to carry out the experiment and simulation.The results show that the convergence speed and accuracy of the proposed algorithm（CGABC）are significantly improved compared with other algorithms such as ABC algorithm,GABC algorithm and so on.
出处
《山东理工大学学报（自然科学版）》
CAS
2017年第5期6-11,17,共7页
Journal of Shandong University of Technology:Natural Science Edition
基金
国家自然科学基金项目(71271071)
安徽省自然科学基金项目(KJ2016A304
KJ2016A308)
关键词
智能算法
交叉
全局
人工蜂群算法
intelligent algorithm
cross
global
artificial bee colony algorithm
