As a new network control and management method for network, software-defined networking (SDN) algorithms have attracted more attention to make networks agile and flexible. To meet the requirements of users and conquer the physical limitation of network, it is necessary to design an efficient controller placement mechanism of SDN, which is defined as an optimization problem to determine the proper positions and number of its controllers. As a modern optimization tool, Quantum-behavior particle swarm optimization (QPSO) algorithm demonstrates power fast convergence rate but limits in global search ability. In this paper, by introducing a full search history and excellent dimension update strategy into the traditional QPSO algorithm which enhances its performance, simulation results show that the proposed algorithm achieves better performance in dozens of different multi-controller placement problems.

软件定义网络（Software-Defined Networking，简称SDN）算法作为一种新型的网络控制和管理网络方法，使网络变得敏捷灵活，越来越受到关注。为了满足用户的需求并克服网络的物理限制，有必要设计一种高效的SDN控制器放置机制，将其定义为一个优化问题，以确定其控制器的适当位置和数量。作为一种现代优化工具，量子行为粒子群优化（QPSO）算法展示了强大的收敛速度，但全局搜索能力有限。在本文中，通过在传统的 QPSO 算法中引入完整的搜索历史和优秀的维度更新策略来提高其性能，