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

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.