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Because of the shortcomings of the general ant colony algorithm in the vehicle scheduling problem such as being slow convergence in the early stages, proposes a new hybrid algorithm combining genetic algorithm with implicit parallel function. The encoding and mutation operation basing on the ant colony algorithm improves the efficiency of solving the optimal distribution path. The vehicle scheduling model and the experimental data shows that the hybrid ant colony algorithm has not only faster converge speed but also the ability to obtain the global optimal solution in a relatively short period.
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