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Differential evolution algorithm is a heuristic global optimization technique based on population. But conventional differential evolution algorithm has a problem of stagnation that can stop the algorithm convergence. Although various versions of adaptive differential evolution algorithms have emerged, it does not take into account whether the fitness of the contemporary individual approximates to the fitness of the optimal individual. An adaptive differential evolution algorithm FMDE is proposed. In order to take advantage of different algorithms, a hybrid optimization algorithm is proposed, based on the combination of different evolution and particle swarm optimization. Finally, the performance is tested and analyzed by using the data of instance. The results show that PSO_FMDE can determine mutation rate adaptively, which enhances the probability of obtaining the global optimum. Comparing with single algorithm, PSO_FMDE algorithm has better performance and is easier to get close to the global optimal solution.