NDLI: Cloud-Computing-Based Resource Allocation Research on the Perspective of Improved Ant Colony Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Weihua Hu Ke Li Junjun Xu Qian Bao
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Technol., Hangzhou Dianzi Univ., Hangzhou, China (Weihua Hu; Ke Li; Junjun Xu; Qian Bao)
Abstract	As a creative intelligent optimization algorithm, ant colony algorithm (ACO) has advantages such as good robustness, positive feedback and distributed computation. It is powerful to solve complicated combinational optimization problems. However, there are many defections existing in a single ACO such as slow solving speed at the primary stage, poor convergence accuracy and easy falling into a local optimal solution. By effectively integrating ACO and genetic algorithm (GA), the presented paper utilized the rapid searching ability of GA to make up the shortage of initial pheromone and increase the convergence speed of the ACO. The experimental result of the simulation tool MATLAB presents that, compared with the traditional GA, ACO is more efficient to solve resource allocating problems.
Starting Page	76
Ending Page	80
File Size	184868
Page Count	5
File Format	PDF
e-ISBN	9781467391665
DOI	10.1109/CSMA.2015.22
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Cloud computing Heuristic algorithms Resource allocation Distributed system Convergence Genetic algorithms Computer science Ant colony algorithm (ACO) Genetic algorithm (GA) Cities and towns Style Resource management
Content Type	Text
Resource Type	Article

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