摘要
在无线充电传感器网络中,移动无线充电设备(WCE)充电路径规划是关键。考虑WCE能量受限以及网络中存在充电瓶颈节点的情况,提出了一种基于自适应蚁群算法的WCE充电路径优化方法。通过引入信息素自适应动态更新机制,使信息素浓度与挥发因子正相关,则路径上的信息素浓度不会太集中,以避免算法陷入局部最优。然后,根据上一次迭代获得的解,动态调整信息素浓度因子,以加强优秀种群的探索能力。实验结果表明,与其他几种典型群智能优化算法相比,本算法收敛速度更快,生成的充电路径长度更短,WCE能耗更少,从而能获得更好的WCE充电路径规划方案。
In wireless rechargeable sensor networks,an important problem is the charging path planning of mobile wireless charging devices.Considering the limited energy of WCE and the existence of charging bottleneck nodes in the network,an adaptive ant colony algorithm based charging path optimization method for WCE is proposed in this paper.By introducing the pheromone adaptive dynamic updating mechanism,the pheromone concentration is positively correlated with volatile factors,so that the pheromone concentration on the path will not be too concentrated,so as to avoid the algorithm falling into local optimization.Then,according to the solution obtained in the last iteration,the pheromone concentration factor was dynamically adjusted to enhance the exploration ability of the excellent population.Experimental results show that compared with other typical swarm intelligent optimization algorithms,the proposed algorithm converges faster,generates a shorter charging path length,and consumes less WCE energy,thus obtaining a better WCE charging path planning scheme.
作者
徐结海
谭德坤
XU Jiehai;TAN Dekun(School of Information Engineering;Jiangxi Province Key Lab of Water Information Cooperative Sensing and Intelligent Processing,Nanchang Institute of Technology,Nanchang 330099,China)
出处
《南昌工程学院学报》
CAS
2023年第1期88-94,101,共8页
Journal of Nanchang Institute of Technology
基金
江西省教育厅科技项目(编号GJJ190958)。
关键词
信息素
蚁群算法
路径规划
无线可充电传感器网络
pheromone
ant colony algorithm
path planning
wireless rechargeable sensor networks
