基于HPSO的自适应路由节点优化部署策略

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.

Adaptive routing node deployment strategy based on hybrid particle swarm algorithm

Aiming at the problems of complicated deployment structure of routing nodes in wireless networks, high economic cost and poor communication quality, an adaptive routing node deployment strategy (ADS) based on optimized hybrid particle swarm algorithm (HPSO) is proposed. This paper takes the lowest deployment cost as the algorithm optimization goal, takes wireless networking node communication, spatial coverage integrity and other characteristics as constraints, through optimizing HPSO combined with ADS, to obtain the best routing node deployment within the application range. First, establish the deployment cost model of the wireless communication network routing node, the deployment communication distance relationship model, the node communication load model, and the free space loss model. According to the model, determine the algorithm optimization target and algorithm restriction conditions. Then the HPSO is optimized, and the elimination mechanism and the diversity supplement mechanism are added to improve the accuracy of algorithm optimization without reducing the efficiency of the algorithm. For spatially adjacent routing nodes, ADS is designed and deployed, and the visual domain model is optimized at the same time to reduce the range of feasible points in ADS and improve the deployment efficiency of the next node. In this method, HPSO and genetic algorithm (GA) algorithm and artificial immune algorithm (AIA) are combined with ADS for comparative experiments. The simulation results show that the method in this paper improves the algorithm efficiency by 14~33% and reduces the deployment cost of routing nodes by 8~10% on the basis of ensuring the communication quality of the wireless communication network.