[1]王守娜,刘弘,高开周.一种应用于函数优化问题的多种群人工蜂群算法[J].郑州大学学报(工学版),2018,39(06):30-35.
 A Multi-swarm Artificial Bee Colony Algorithm for Function Optimization[J].Journal of Zhengzhou University (Engineering Science),2018,39(06):30-35.
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一种应用于函数优化问题的多种群人工蜂群算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
39
期数:
2018年06期
页码:
30-35
栏目:
出版日期:
2018-10-24

文章信息/Info

Title:
A Multi-swarm Artificial Bee Colony Algorithm for Function Optimization
作者:
王守娜刘弘高开周
文献标志码:
A
摘要:
针对传统人工蜂群算法(ABC)收敛速度慢、易陷入局部最优等不足,提出一种基于种群分割的多种群人工蜂群算法(MABC)应用于函数优化问题.该算法利用K均值聚类算法中基于欧氏距离的方式对人工蜂群进行种群分割,在子种群中引入基于全局通信的蜜源位置更新方式加速算法收敛,同时引入基于局部通信的适应度函数扩展解方案的多样性.通过对6个标准测试函数的实验表明,MABC算法适应度高、收敛速度快,克服了ABC算法易陷入局部最优解等不足,在函数优化问题中表现出了更好的性能.
Abstract:
A multi-swarm Artificial Bee Colony(MABC)algorithm based on the segmentation of population was proposed in this paper. It was applied to function optimization to overcome the drawbacks of slow convergence and low computational accuracy of conventional ABC algorithm. ?In this algorithm, K-means clustering algorithm based on Euclidean distance was introduced to divide the bee colony. In the subpopulation, ?a method was introduced to update the location of nectar based on global communication to accelerate the convergence of the algorithm;and the fitness function based on local communication was introduced to expand the diversity of the solution. The simulation results of six standard functions show that the MABC algorithm can attain significant improvement on convergence rate and solution accuracy, and show better performance in function optimization problems when compared with the ABC algorithm.
更新日期/Last Update: 2018-10-25