摘要
针对QoS组播路由的最优求解问题,提出一种改进量子遗传算法.首先使用将图形网络拓扑简化为树形网络拓扑,并在种群初始化过程中引入基于概率划分的小生境协同进化策略.然后设计了新的量子旋转门调整规则,以便实时处理量子旋转角,从而提高量子搜索的收敛速度并增加了种群的多样性,然后采用基于锦标赛选择机制的灾变算子,以便全局寻优和收敛速度能够得到有效平衡.最后,将该算法与其他智能启发算法进行仿真对比.实验仿真结果表明:改进后的量子遗传算法能获得比其他智能启发算法更优的解,同时具有更快的收敛速度和较好的全局寻优能力.
An improved quantum genetic algorithm is proposed for the optimal solution of QoS multicast routing.Firstly,the minimum cost multicast tree algorithm is used to simplify the graph network topology into a tree network topology,and a niche co-evolution strategy based on probability partitioning is introduced in the population initialization process.Then a new quantum revolving door adjustment rule is designed to process the quantum rotation angle in real time,which improves the convergence speed of quantum search and increases the diversity of the population.Then,the catastrophe operator based on the tournament selection mechanism is adopted to overcome the premature phenomenon.Finally,the algorithm is compared with other intelligent heuristic algorithms.The experimental results show that the improved quantum genetic algorithm can obtain better solutions than other intelligent heuristic algorithms,and has faster convergence speed and better global optimization ability.
作者
崔玉胜
CUI Yusheng(Department of Information Management,Minnan University of Science and Technology,Fujian 362700,China)
出处
《泉州师范学院学报》
2019年第6期45-50,共6页
Journal of Quanzhou Normal University
基金
福建省教育厅科技规划项目(JAT160595).
关键词
量子计算
遗传算法
QOS组播路由
收敛速度
全局寻优
quantum computing
genetic algorithm
QoS multicast routing
convergence rate
global optimization
