基于HPSO的自适应路由节点优化部署策略

Optimal Deployment Strategy of Adaptive Routing Nodes Based on HPSO

针对无线网络中的路由节点的部署结构冗杂,经济成本高,通信质量差的问题,提出了一种基于优化混合粒子群算法(HPSO)的自适应路由节点部署策略(ADS);以最低部署成本为算法寻优目标,以无线组网节点通信,空间覆盖完整性等特点为限制条件,通过优化HPSO结合ADS,得到应用范围内的最佳的路由节点部署;首先建立无线通信网络路由节点的部署成本模型,部署通信距离关系模型,节点通信负载模型,自由空间损耗模型;依据模型确定算法寻优目标及算法限制条件;然后对HPSO进行优化,加入淘汰机制和多样性补充机制,在不降低算法效率(寻优时间)的基础上提升算法寻优准确度;对于空间相邻的路由节点,设计并采用ADS进行部署,同时优化可视域模型,缩小ADS中可行点集范围,提高下一节点的部署效率;文章方法中的HPSO与遗传算法(GA)算法和人工免疫算法(AIA)分别结合ADS进行对比试验;仿真结果表明,文章方法在保证无线通信网络通信质量的基础上,提升14%~33%算法效率,降低8%~10%的路由节点部署成本。

Aiming at the problems of redundant deployment structure,high economic cost,and poor communication quality of routing nodes in wireless networks,and adaptive routing node deployment strategy(ads)based on optimized hybrid particle swarm optimization(HPSO)is proposed.It takes the lowest deployment cost as the algorithm optimization goal,takes the characteristics of wireless network node communication and spatial coverage integrity as the limiting conditions,and obtains the best routing node deployment in the application range by optimizing HPSO combined with ads.Firstly,the deployment cost model,deployment communication distance relationship model,node communication load model,and free space loss model of routing nodes in a wireless communication network are established.According to the model,the optimization objectives and algorithm constraints are determined.Then,HPSO is optimized,and elimination mechanism and diversity supplement mechanism are added to improve the optimization accuracy without reducing the efficiency of the algorithm.For spatially adjacent routing nodes,ads are designed and used for deployment,and the visual domain model is optimized to reduce the range of feasible point sets in ads and improve the deployment efficiency of the next node.In this paper,HPSO is compared with genetic algorithm(GA)and artificial immune algorithm(AIA)combined with ads.The simulation results show that the proposed method improves the algorithm efficiency by 14%~33%and reduces the deployment cost of routing nodes by 8%~10%based on ensuring the communication quality of the wireless communication networks.