无线传感器网络中无人机充电效率的多目标优化方法

Multi-objective Optimization Method for UAV Charging Efficiency in Wireless Sensor Network

摘要

针对如何在机载能量有限的情况下合理分配能量资源以提高无人机(unmanned aerial vehicle, UAV)在三维无线传感器网络中充电效率的问题,构建了一个联合UAV充电时间和网络设备获得的充电能量的多目标优化模型,通过优化UAV的部署位置和悬停时间,实现充电时间最小化与充电能量最大化。该模型的解空间具有完全离散性,需要进行全局搜索求解,因此,提出一种基于混沌理论、灰狼优化(grey wolf optimization, GWO)算法的更新策略和基于柯西算子变异方法的增强型多目标粒子群优化(enhanced multi-objective particle swarm optimization, EMOPSO)算法。通过混沌理论初始化UAV对应的解向量,使其在解空间中均匀分布,并在算法迭代更新时引入GWO和柯西算子,增强了解向量跳出局部最优的能力。仿真结果表明,改进的算法可以提升三维无线传感器网络解空间中的全局搜索能力,相较于其他优化算法具有较好的求解精度。

Abstract

To solve the problem of how to reasonably allocate energy resources to improve the charging efficiency of unmanned aerial vehicle (UAV) in three-dimensional wireless sensor networks when the onboard energy is limited, a multi-objective optimization model that combines the charging time of UAV and the charging energy obtained by devices is constructed. By jointly optimizing the deployment position and hovering time of UAV, the charging time was minimized while maximizing the charging energy. Since the solution space of this model was purely discrete, it was necessary to perform a global search. Therefore, the enhanced multi-objective particle swarm optimization (EMOPSO) algorithm based on chaos theory, the grey wolf optimization (GWO) algorithm update strategy and the Cauchy operator mutation method was proposed. The solution vector corresponding to UAV was initialized by chaos theory to make it evenly distributed in the solution space. GWO and Cauchy operator were introduced during the iterative update of the algorithm to enhance the ability of the solution vector to jump out of the local optimum. Simulation results show that EMOPSO can improve the ability of global search of the solution space in networks, and has better solution accuracy than other algorithms.

关键词

无线传感器网络 / 无人机(unmanned aerial vehicle, UAV) / 充电效率 / 多目标优化模型 / 多目标粒子群优化(multi-objective particle swarm optimization, MOPSO)算法 / 灰狼优化(grey wolf optimization, GWO)算法

Key words

wireless sensor network / unmanned aerial vehicle (UAV) / charging efficiency / multi-objective optimization model / multi-objective particle swarm optimization (MOPSO) algorithm / grey wolf optimization (GWO) algorithm

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基金资助

国家自然科学基金资助项目(62272194)

吉林省科技发展计划重点研发资助项目(20230201087GX)