Task scheduling is a key problem concerned in computational grid. In this paper, a heuristic approach based on particle swarm optimization is adopted to solving scheduling problem in grid environment. Each particle is represented a possible solution, and the position vector is transformed from the continuous variable to the discrete variable. This approach aims to generate an optimal schedule so as to get the minimum makespan and maximum resource utilization while completing the tasks. The results of simulated experiments show that the particle swarm optimization algorithm is able to get the better schedule than genetic algorithm
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