摘要
为了降低计算机网络的时延和运营费用以改进网络性能,首次采用一种改进的粒子群算法优化计算机网络中路由选择和链路容量与流量分配(CFA)问题。将改进算法的惯性权重改进为线性衰减的变化权重,加入了线性变化的学习因子、模拟退火机制、变异操作及邻域搜索策略,提高了算法的性能。计算机仿真结果表明,同传统优化算法相比该方法对求解网络的路由选择和CFA问题具有很大优越性。研究结果不仅对各类网络的优化问题有一定的应用价值,而且也扩展了粒子群算法的应用范围。
In order to lower delay and operational costs and improve the performance of computer networks, the problems of route selection and link capacity and flow assignments （CFA） in computer network are firstly optimized by using an improved particle swarm optimization （PSO） algorithm. The inertia weight factor is improved to linearly attenuate varying weight in the improved algorithm, and the linearly varying studying factor, the simulated annealing mechanism, the mutation operation and neighboring search strategy are added to raising performance in the algorithm. The computer simulated results show that compared with some traditional optimizing method, the great superiority of the improved PSO is further shown in solved problems of route selection and CFA in networks, The investigating results are of certain application values not only for the various network optimization problems, but also application range of the PSO algorithm is expanded.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第14期3577-3578,3676,共3页
Computer Engineering and Design
关键词
计算机通信网络
粒子群算法
改进
路由选择
容量与流量分配
computer communication networks
particle swarm optimization algorithm
improvement
route selection
capacity and flow assignment （CFA）
