A nt c olony method for c ost and t ime

In this paper, we would like to propose an improved algorithm for task scheduling in grid environment with metaheuristic ant colony optimization method considering the cost and time parameters from quality of service. The proposed algorithm is evaluated by using the required cost and time parameters to carry out the task. With implementation these parameters in the simulate environment, we can create situation that scheduling task will be done with better position and achieve high performance on computational grids. Finally the experiment and simulated results will show that the proposed heuristic scheduling algorithm performs significantly to ensure high throughput, reduced time and cost. Also proposed algorithm is more efficient in the grid environment. This proposed algorithm is more efficient than the adaptive ACS and MOACO algorithms.