With the development of the fifth-generation wireless network, autonomous moving platforms such as unmanned aerial vehicles (UAV) have been widely used in modern smart cities. In some applications, the UAVs need to perform certain monitoring tasks within a specified time. However, due to the energy constraints of UAVs, such tasks require using multiple UAVs to monitor multiple points. To solve this practical problem, this paper proposes a multi-UAV path planning model with the energy constraint (MUPPEC). The MUPPEC considers the energy consumption of a UAV in different states, such as acceleration, cruising speed, deceleration, and hovering, and the main objective of the MUPPEC is to minimize the total monitoring time. Also, a hybrid discrete intelligence algorithm based on the grey wolf optimizer (HDGWO) is proposed to solve the MUPPEC. In the HDGWO, the discrete grey wolf update operators are implemented, and the integer coding and greedy algorithms are used to transform between the grey wolf space and discrete problem space. Furthermore, the central position operation and stagnation compensation grey wolf update operation are introduced to improve the global convergence ability, and a two-opt with azimuth is designed to enhance the local search ability of the algorithm. Experimental results show that the HDGWO can solve the MUPPEC effectively, and compared to the traditional grey wolf optimizer(GWO), the discrete operators and the two-opt local search strategy with azimuth can effectively improve the optimization ability of the GWO.

随着第五代无线网络的发展，无人驾驶飞行器（UAV）等自主移动平台在现代智慧城市中得到广泛应用。在某些应用中，无人机需要在规定的时间内完成一定的监控任务。但是，由于无人机的能量限制，此类任务需要使用多架无人机来监控多个点。针对这一实际问题，本文提出了一种具有能量约束的多无人机路径规划模型（MUPPEC）。MUPPEC 考虑了无人机在加速、巡航速度、减速和悬停等不同状态下的能耗，MUPPEC 的主要目标是最小化总监控时间。此外，提出了一种基于灰狼优化器（HDGWO）的混合离散智能算法来解决MUPPEC。HDGWO中实现了离散灰狼更新算子，利用整数编码和贪心算法在灰狼空间和离散问题空间之间进行转换。此外，引入中心位置操作和停滞补偿灰狼更新操作以提高全局收敛能力，并设计具有方位角的二选一以增强算法的局部搜索能力。实验结果表明，HDGWO可以有效地解决MUPPEC问题，与传统的灰狼优化器（GWO）相比，离散算子和带有方位角的二选局部搜索策略可以有效提高GWO的优化能力。使用整数编码和贪心算法在灰狼空间和离散问题空间之间进行转换。此外，引入中心位置操作和停滞补偿灰狼更新操作以提高全局收敛能力，并设计具有方位角的二选一以增强算法的局部搜索能力。实验结果表明，HDGWO可以有效地解决MUPPEC问题，与传统的灰狼优化器（GWO）相比，离散算子和带有方位角的二选局部搜索策略可以有效提高GWO的优化能力。使用整数编码和贪心算法在灰狼空间和离散问题空间之间进行转换。此外，引入中心位置操作和停滞补偿灰狼更新操作以提高全局收敛能力，并设计具有方位角的二选一以增强算法的局部搜索能力。实验结果表明，HDGWO可以有效地解决MUPPEC问题，与传统的灰狼优化器（GWO）相比，离散算子和带有方位角的二选局部搜索策略可以有效提高GWO的优化能力。并设计了带有方位角的二选一，以增强算法的局部搜索能力。实验结果表明，HDGWO可以有效地解决MUPPEC问题，与传统的灰狼优化器（GWO）相比，离散算子和带有方位角的二选局部搜索策略可以有效提高GWO的优化能力。并设计了带有方位角的二选一，以增强算法的局部搜索能力。实验结果表明，HDGWO可以有效地解决MUPPEC问题，与传统的灰狼优化器（GWO）相比，离散算子和带有方位角的二选局部搜索策略可以有效提高GWO的优化能力。