Ant colony algorithm of multi-objective optimization for dynamic grid scheduling

A method for grid scheduling is proposed to optimize multiple objectives based on ant colony algorithm. Ant colony algorithm is a global optimization method and has the advantages of parallel search and positive feedback. But the algorithm is prone to stagnate or be trapped into a local optimum. This paper introduces the solution space information to improve the performance. The capacity of grid resource is exploited to produce the initial pheromone and the local pheromone and global pheromone are adjusted according to the workload in the late stage to maintain the load balance. The task cost is estimated as heuristic information when tasks are assigned to different grid resources to prevent the occurrence of premature and stagnation. The algorithm is realized in Gridsim environment and the simulation results prove that the proposed algorithm is superior to some heuristic algorithms. © Metallurgical and Mining Industry, 2015.