Cloud-Computing-Based Resource Allocation Research on the Perspective of Improved Ant Colony Algorithm
Authors:
- Weihua HuWHWeihua Hu
- Ke LiKLKe Li
- Junjun XuJXJunjun Xu
- Qian BaoQBQian Bao
- 1 October 2015
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 76-80
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.Keywords
This publication has 1 reference indexed in Scilit:
- Efficient Hierarchical Parallel Genetic Algorithms using Grid computingFuture Generation Computer Systems, 2007