In order to improve the accuracy and efficiency of parallel differential evolution (DE), this paper proposes a parallel differential evolution with multi-population and multi-strategy, which provides a way to rebustly address various optimization problems. This algorithm divides an initial population into several sub-populations, and then they evolve with different DE strategies. The sub-populations evolve independently at first, and then communicate with each other at regular intervals. By using the proposed multi-population and multi-strategy, the parallel realization of the algorithm can save the computation time while searching with different optimization strategies. The experi- mental results show that the proposed algorithm is feasible and effective for solving different optimization problems.%为了更好地提高并行差分进化算法的求解精度和计算效率，实现适用于解决多种优化问题的鲁棒性算法，提出了一种多种群多策略的并行差分进化算法。该算法将种群划分为多个子种群，不同的子种群分别采用不同的差分进化策略。多个子种群各自独立进化，互不干扰，每隔一定代数才进行种群间的通信交流。通过利用多种群实现多种优化策略，并采用并行方式，使得算法可以采用不同的优化策略进行搜索，更加节省计算时间。数值实验结果表明，该算法在求解不同类型的优化问题时都具有良好的计算能力和效率。
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