.png){kind=icon}
返回
Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-012-0957-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The artificial bee colony has the advantage of employing fewer control parameters compared with other population-based optimization algorithms. In this paper a binary artificial bee colony (BABC) algorithm is developed for binary integer job scheduling problems in grid computing. We further propose an efficient binary artificial bee colony extension of BABC that incorporates a flexible ranking strategy (FRS) to improve the balance between exploration and exploitation. The FRS is introduced to generate and use new solutions for diversified search in early generations and to speed up convergence in latter generations. Two variants are introduced to minimize the makepsan. In the first a fixed number of best solutions is employed with the FRS while in the second the number of the best solutions is reduced with each new generation. Simulation results for benchmark job scheduling problems show that the performance of our proposed methods is better than those alternatives such as genetic algorithms, simulated annealing and particle swarm optimization.
Keyword:
Artificial bee colony (ABC)
Binary artificial bee colony (BABC)
Efficient binary artificial bee colony (EBABC)
Flexible ranking strategy (FRS)
Job scheduling
Grid computing
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
Multi-circle detection on images using artificial bee colony (ABC) optimization基于人工蜂群 (ABC) 优化的图像多圆检测.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏Friction modeling and compensation for haptic master manipulator based on deep Gaussian process
PREAI
分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏
查看更多内容