Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms

作     者：Siling Feng Yinjie Chen Mengxing Huang Feng Shu 

作者机构：School of Information and Communication EngineeringHainan UniversityNo.58 Renmin AvenueHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China SeaHainan UniversityNo.58 Renmin AvenueHaikou570228China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第75卷第5期

页      面：4341-4355页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 082503[工学-航空宇航制造工程] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was funded by the National Key Research and Development Program of China under Grant 2018YFB1404400 

主　　题：Resource allocation unmanned aerial vehicles harris hawks optimization whale optimization algorithm 

摘      要：Due to their adaptability,Unmanned Aerial Vehicles(UAVs)play an essential role in the Internet of Things(IoT).Using wireless power transfer(WPT)techniques,an UAV can be supplied with energy while in flight,thereby extending the lifetime of this energy-constrained *** paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed *** this paper,we propose an optimization strategy for the resource allocation of UAVs in sensor communication *** is a practical solution to the problem of marine sensor networks that are located far from shore and have limited power.A corresponding system model is summarized based on the scenario and existing theoretical *** minimum throughputmaximizing object is then formulated as an optimization *** swarm intelligence algorithms are utilized effectively in numerous fields,this paper chose to solve the formed optimization problem using the Harris Hawks Optimization and Whale Optimization *** paper introduces a method for translating multi-decisions into a row vector in order to adapt swarm intelligence algorithms to the problem,as joint time and energy optimization have two sets of *** proposed method performs well in terms of stability and ***,performance is evaluated through numerical *** results demonstrate that the proposed method performs admirably in the given scenario.