布谷鸟粒子群混合算法  

Mixed Algorithm of Cuckoo Search and Particle Swarm Optimization

作　　者：杨小东[1] 牛俊英[1] 蔡泽凡[1] YANG Xiaodong;NIU Junying;CAI Zefan(School of Electronics and Information Engineering,Shunde Polytechnic,Foshan Guangdong 528333,China)

机构地区：[1]顺德职业技术学院电子与信息工程学院,广东佛山528333

出　　处：《顺德职业技术学院学报》2018年第3期10-16,共7页Journal of Shunde Polytechnic

基　　金：佛山市智能制造工程技术研究中心;广东省数字化工厂工程技术研究中心;广东省自然科学基金项目(2015A030313225)

摘　　要：布谷鸟搜索算法和粒子群优化算法都属于仿生优化群算法,它们的原理简单、实现方便,在诸多领域得到应用。虽然这两种算法优点明显,但是它们在全局搜索能力、收敛速度等方面存在不同程度的不足,当它们应用于复杂优化问题时,需要采用改进措施来提升其性能。把布谷鸟搜索算法和粒子群优化算法进行混合,在两种算法平行进化的基础上引入共享机制,使两种算法优点互补。仿真证明,混合算法提升了算法的全局搜索能力和收敛速度,适应性更强,可以应用于复杂的优化问题。Both cuckoo search algorithm and particle swarm optimization algorithm are bionic optimization group algorithms. Their principles are simple and easy to implement, and they are applied in many fields. Although these two algorithms have obvious advantages, they have different degrees of weakness in global search capability and convergence speed. When they are applied to complex optimization problems, their performances need to be improved. In this paper, the cuckoo search algorithm and the particle swarm optimization algorithm are mixed, and the sharing mechanism is introduced on the basis of the parallel evolution of the two algorithms, so that the advantages of the two algorithms are complementary. Simulation results show that the hybrid algorithm improves the global search ability and convergence speed, and is more adaptable. It can be applied to complex optimization problems.

关 键 词：布谷鸟搜索算法 粒子群优化算法 混合算法 混沌 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]