低轨卫星网络凭借时延小、成本低、损耗小和全球覆盖等优点成为了地面网络的有力补充.然而,用户分布不均、全网流量随时间变化,可能会导致部分链路出现拥塞、负载不均衡的问题.基于以上问题,提出一种基于遗传优化和人工蜂群(Genetic Optimization and Artificial Bee Colony,GABC)的低轨卫星网络负载均衡路由算法,将遗传优化算法与人工蜂群算法相结合,避免人工蜂群算法陷入局部最优,并设置抗拥塞机制来解决负载拥塞问题.基于NS2 仿真平台实现GABC算法,实验结果表明,相比最短路径路由(Shortest Path Routing,SPR)算法和蚁群智能路由LBRA-CP算法,该算法在保证较低时延和开销比的同时拥有更高的数据传输率和吞吐量.
Load Balancing Routing Algorithm Based on Genetic Optimization and Artificial Bee Colony in LEO Satellite Network
The LEO satellite network become a powerful complement to the terrestrial network as it shows the advantage of low latency,low cost,low loss rate and global coverage.However,due to the uneven distribution of users and the variation of network traffic over time,some links may be congested and unbalanced.A load balancing routing algorithm based on genetic optimization and artificial bee colony in LEO satellite networks(GABC)was proposed,which combined genetic algorithm with artificial bee colony algorithm to avoid local optimization.Experimental results showed that compared with the shortest path routing and LBRA-CP algorithm,the proposed algorithm has higher data transmission rate and throughput with lower delay and overhead ratio.
LEO satellite networkartificial bee colony algorithmgenetic optimization algorithm
国家科技期刊平台
NSTL
万方数据
