Abstract: In order to decrease operation costs and improve the performance of networks, a new and efficient swarm intelligence search method based on particle swarm optimization algorithm for solving the problem of link Capacity and Flow Assignment(CFA) in computer communication networks is presented. Results of a great number of computer simulation experiments show that, not only the effectiveness of particle swarm optimization algorithm for solving CFA problem is borne out, but also, compared with the traditional lagrangean relaxation and subgradient optimization method, the obtained solution has higher accuracy, and compared with the genetic algorithm, the superiority of particle swarm optimization algorithm is further shown in the complexity and running speed.

Key words: capacity and flow assignments, combinatorial optimization, particle swarm optimization algorithm

摘要： 为了降低网络运营费用与改进网络性能，采用近年来新出现的一种高效的群智能搜索方法——粒子群优化算法求解计算机通信网络中链路容量与流量分配(CFA)问题。大量的计算机仿真实验结果验证了该算法在CFA问题中的有效性，而且与传统的拉格朗日松弛及子梯度寻优算法相比，解的质量有了大幅度的提高。与遗传算法相比，该算法在复杂性及运行速度等方面更具优越性。

关键词: 容量与流量分配, 组合优化, 粒子群优化算法

CLC Number: 

• TP393