Research on Grid Resources Schedule Based on an Adaptive Distribute Parallel Genetic Algorithm

In this paper an improved adaptive parallel genetic algorithm is proposed to solve problems of grid resources distribution and matching, comparing with the traditional genetic algorithms, a new adaptive selection operator is introduced, which can prevent the premature convergence of genetic algorithm efficiently. Besides, in this paper, the migration strategy of the parallel genetic algorithm can prevent the population trapped in the local extreme. And a pc-cluster containing eight computers is constructed to execute the coarse-grained parallel genetic algorithm and series genetic algorithm, and different scale resources and tasks are tested on the pc-cluster. Several examples are provided to be examined and the results illustrate that the proposed algorithm has higher global optimization capability, computational efficiency and stronger stability than the traditional genetic algorithm for the max time span. From these results, the parallel genetic algorithm reduced the searching time much more than series genetic algorithm for the same solutions. Moreover, compared with series genetic algorithm, the parallel genetic algorithm can get the more optimal solutions when the iteration is same.