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One of the open issues in grid computing is efficient job scheduling. Job scheduling is known to be NP-complete. Therefore, the use of non-heuristics is the de facto approach in order to cope in practice with its difficulty. In this paper, the authors propose a Modified Artificial Fish Swarm Algorithm (MAFSA) for job scheduling. The basic idea of AFSA is to imitate the fish behaviors such as preying, swarming, and following with local search of fish individual for reaching the global optimum. The results show that the method is insensitive to initial values, has a strong robustness and has the faster convergence speed and better estimation precision than the estimation method by Genetic Algorithm (GA) and Simulated Annealing (SA).
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