High-density wireless sensor networks (HDWSNs) are usually deployed randomly, and each node of the network collects data from complex environments. Because the energy of sensor nodes is powered by batteries, it is basically impossible to replace batteries or charge in the complex surroundings. In this paper, a QoS routing energy consumption model is designed, and an improved adaptive elite ant colony optimization (AEACO) is proposed to reduce HDWSN routing energy consumption. This algorithm uses the adaptive operator and the elite operator to accelerate the convergence speed. So, as to validate the efficiency of AEACO, the AEACO is contrast with particle swarm optimization (PSO) and genetic algorithm (GA). The simulation outcomes show that the convergence speed of AEACO is sooner than PSO and GA. Moreover, the energy consumption of HDWSNs using AEACO is reduced by 30.7% compared with GA and 22.5% compared with PSO. Therefore, AEACO can successfully decrease energy consumption of the whole HDWSNs.

中文翻译：

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基于自适应精英蚁群算法的高密度无线传感器网络路由能耗最小化

高密度无线传感器网络（HDWSN）通常是随机部署的，网络的每个节点都从复杂的环境中收集数据。由于传感器节点的能量由电池供电，因此基本上不可能在复杂的环境中更换电池或充电。本文设计了一种QoS路由能耗模型，并提出了一种改进的自适应精英蚁群优化算法（AEACO）来降低HDWSN路由能耗。该算法利用自适应算子和精英算子来加快收敛速度​​。因此，为了验证AEACO的效率，AEACO与粒子群优化（PSO）和遗传算法（GA）形成对比。仿真结果表明，AEACO的收敛速度比PSO和GA快。而且，与GA相比，使用AEACO的HDWSN的能耗降低了30.7％，与PSO相比降低了22.5％。因此，AEACO可以成功降低整个HDWSN的能耗。