期刊名称：International Journal of Grid and Distributed Computing
印刷版ISSN：2005-4262
出版年度：2016
卷号：9
期号：6
页码：1-12
DOI：10.14257/ijgdc.2016.9.6.01
出版社：SERSC
摘要：How to better conduct research resource scheduling has long been a research direction of cloud computing. This paper, aiming at slow convergence and easiness of falling local optimum of ant colony algorithm,has integrated genetic algorithm into the ant colony algorithm and obtained hybrid algorithm (ACA -GA); in the initial solution of the ant colony algorithm, it has adopted selection, crossover and mutation operations of genetic algorithm to obtain an effective initial solution; secondly, it has used the perception threshold of ant colony algorithm path setting to regulate individual selection optimal path; finally, it has improved volatile factor so as to significantly improve the updating efficiency of pheromone. The algorithm in the paper proved that the performance of the algorithm has been also significantly improved through classical test functions. Cloudsim platform shows that, the algorithm above mentioned reduces the time and cost spent in resource scheduling of, hence has some promotional value.
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