中文摘要：

为了提升差分演化算法对局部最优的逃逸能力和避免早熟收敛，设计了一种邻域结构为复杂网络的差分演化算法（CNS-DE）。该算法将复杂网络上每一个节点定义为一个计算个体，节点间的连接关系决定了个体间的交互结构。CNS-DE的差分策略主要基于节点（个体）的邻居关系定义，该策略有利于保持群体的多样性，充分利用了群体分布特性。在函数寻优的经典数据集上，将CNS-DE与传统差分算法进行了对比。结果表明，该算法能有效避免陷入局部最优，有效改善了早熟现象，对解的质量有较大幅度提高。

英文摘要：

In order to improve the capability of escaping local optimum for the differential evolution algorithm, and avoid pre- mature convergence, this paper designed a new algorithm named CNS-DE. The algorithm adopted a complex network as its spatial structure. Specifically, CNS-DE put an individual on one node of the network; the individual evolved by mainly inter- acting with its neighbors on the network. Based on nodes＇ （ individuals＇ ） neighbor relationship, this paper proposed a new differential strategy for CNS-DE. The policy fully used the distribution of group and is conductive to maintaining population di- versity. On the classic dataset for the tasks of function optimization, a series of experimental results of CNS-DE and DE show that the new algorithm can effectively avoid getting into local optimum, and effectively improve the precocious phenomenon. In addition, it greatly increases the quality of solutions.