Paper Info
Title
Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization
Abstract
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.
Year
DOI
Venue
2013
10.1007/s00500-012-0957-7
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Keywords
Field
DocType
artificial bee colony,efficient binary artificial bee colony,efficient binary artificial bee colony (ebabc),grid computing,flexible ranking strategy,artificial bee colony (abc),binary artificial bee colony (babc),job scheduling,binary artificial bee colony,flexible ranking strategy (frs)
Simulated annealing,Particle swarm optimization,Population,Artificial bee colony algorithm,Mathematical optimization,Grid computing,Computer science,Job scheduler,Genetic algorithm,Metaheuristic
Journal
Volume
Issue
ISSN
17
5
14337479
Citations 
PageRank 
References 
8
0.51
45
Authors
5
Name
Order
Citations
PageRank
Sung-Soo Kim1263.09
Ji-Hwan Byeon2202.42
Hongbo Liu31426105.95
Ajith Abraham48954729.23
Seán F. McLoone522424.90