基于改进离散果蝇优化算法的WSN广播路由算法

发布时间：2018-05-09 04:21

本文选题：无线传感网络 + 广播路由　；参考：《计算机应用》2017年04期

【摘要】：为解决无线传感网络(WSN)节点能量限制和广播路由的能耗问题,提出一种基于改进离散果蝇优化算法(DFOA)的WSN广播路由算法。首先,将交换子和交换序引入到果蝇优化算法(FOA)中,得到DFOA,拓展FOA的应用领域;然后,利用莱维(Lévy)飞行对果蝇随机探索的步长进行控制,增加DFOA的样本多样性,并用轮盘赌选择对种群的位置更新策略进行改进,避免算法陷入局部最优;最后利用改进DFOA对WSN路由能耗寻优,找到能耗最小的广播路径。仿真结果表明,改进DFOA获得的广播能耗更低,在不同的网络规模下,均优于对比算法(原DFOA、模拟退火遗传算法(SA-GA)、蚁群优化(ACO)算法和粒子群优化(PSO)算法)。改进DFOA能增加种群多样性,增强跳出局部最优的能力,提高网络性能。
[Abstract]:In order to solve the problem of energy limitation and energy consumption of broadcast routing in wireless sensor networks, a WSN broadcast routing algorithm based on improved discrete Drosophila Optimization algorithm (DFOAA) is proposed. Firstly, the commutator and exchange sequence were introduced into Drosophila Optimization algorithm (FOA), and the application field of FOA was expanded. Then, the step size of Drosophila's random exploration was controlled by L 茅 vy flight, and the diversity of DFOA samples was increased. Roulette selection is used to improve the location updating strategy of the population to avoid the algorithm falling into local optimum. Finally the improved DFOA is used to optimize the energy consumption of WSN routing to find the broadcast path with the least energy consumption. The simulation results show that the improved DFOA has lower broadcasting energy consumption and is superior to the contrast algorithm (DFOA, simulated annealing genetic algorithm, Ant Colony Optimization) and Particle Swarm Optimization (PSO) algorithm under different network scales. Improving DFOA can increase population diversity, enhance the ability to jump out of local optimum, and improve network performance.
【作者单位】：贵州大学大数据与信息工程学院;
【基金】：贵州省教育厅项目基金资助项目(黔教合KY字[2016]124) 贵州省科技厅项目基金资助项目(黔科合LH字[2014]7628) 贵州大学博士项目基金资助项目(贵大人基合字[2010]010);贵州大学研究生创新基金资助项目(研理工2016066)~~
【分类号】：TP18;TP212.9;TN929.5

【参考文献】