An effective memetic algorithm for UAV routing and orientation under uncertain navigation environments

Navigation correction is usually frequently required by unmanned aerial vehicles (UAVs), especially under uncertain navigation environments. Although the UAV’s straight flights that connect navigation correction points can constitute a plan of navigation corrections, the underlying attitude orientat...

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Vydáno v:	Memetic computing Ročník 13; číslo 2; s. 169 - 183
Hlavní autoři:	Xiang, Shang, Wang, Ling, Xing, Lining, Du, Yonghao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021 Springer Nature B.V
Témata:	Algorithms Applications of Mathematics Artificial Intelligence Bioinformatics Complex Systems Control Engineering Heuristic methods Knowledge bases (artificial intelligence) Mathematical and Computational Engineering Mechatronics Navigation Optimization Regular Research Paper Robotics Steering Unmanned aerial vehicles Navigation correction Memetic algorithm Routing Uncertainty Unmanned aerial vehicle Orientation
ISSN:	1865-9284, 1865-9292
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Abstract	Navigation correction is usually frequently required by unmanned aerial vehicles (UAVs), especially under uncertain navigation environments. Although the UAV’s straight flights that connect navigation correction points can constitute a plan of navigation corrections, the underlying attitude orientations of the UAV when flying through the visited points are also required by appropriate steering motions. In this regard, a UAV routing and orientation problem (UAV-ROP) that minimizes the 3D flight distances of the UAV under navigational, steering and uncertain constraints, is introduced and proven NP-hard in this paper. To optimize the layered routing and orientations in the UAV-ROP simultaneously, an effective memetic algorithm is proposed in this paper. In the algorithm, the GA performs the outer loop for optimizing the route and the local search metaheuristic does the inner loop for optimizing the orientations. Also, a globally maintained knowledge base that records high-quality sub-routes is used to accelerate the inner optimization of the memetic algorithm. The highlight in addressing the UAV-ROP in this paper is a layered optimization idea in a memetic algorithm to fit the layered optimization requirements of the problem. Experiments on open-access datasets indicate that, the proposed memetic algorithm shows an excellent overall performance compared with other competitors, which is qualified to give an authenticated reliable route with orientations of the UAV despite uncertain navigation environments.
AbstractList	Navigation correction is usually frequently required by unmanned aerial vehicles (UAVs), especially under uncertain navigation environments. Although the UAV’s straight flights that connect navigation correction points can constitute a plan of navigation corrections, the underlying attitude orientations of the UAV when flying through the visited points are also required by appropriate steering motions. In this regard, a UAV routing and orientation problem (UAV-ROP) that minimizes the 3D flight distances of the UAV under navigational, steering and uncertain constraints, is introduced and proven NP-hard in this paper. To optimize the layered routing and orientations in the UAV-ROP simultaneously, an effective memetic algorithm is proposed in this paper. In the algorithm, the GA performs the outer loop for optimizing the route and the local search metaheuristic does the inner loop for optimizing the orientations. Also, a globally maintained knowledge base that records high-quality sub-routes is used to accelerate the inner optimization of the memetic algorithm. The highlight in addressing the UAV-ROP in this paper is a layered optimization idea in a memetic algorithm to fit the layered optimization requirements of the problem. Experiments on open-access datasets indicate that, the proposed memetic algorithm shows an excellent overall performance compared with other competitors, which is qualified to give an authenticated reliable route with orientations of the UAV despite uncertain navigation environments.
Author	Du, Yonghao Xiang, Shang Xing, Lining Wang, Ling
Author_xml	– sequence: 1 givenname: Shang surname: Xiang fullname: Xiang, Shang organization: College of Systems Engineering, National University of Defense Technology – sequence: 2 givenname: Ling surname: Wang fullname: Wang, Ling organization: Department of Automation, Tsinghua University – sequence: 3 givenname: Lining orcidid: 0000-0002-6983-4244 surname: Xing fullname: Xing, Lining email: xinglining@gmail.com organization: College of Systems Engineering, National University of Defense Technology, State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology – sequence: 4 givenname: Yonghao surname: Du fullname: Du, Yonghao organization: College of Systems Engineering, National University of Defense Technology
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Cites_doi	10.1007/s12293-019-00291-4 10.1016/j.trpro.2016.02.007 10.1016/j.cie.2019.05.038 10.1016/j.endm.2018.01.008 10.1109/TASE.2018.2867614 10.1016/j.omega.2018.01.001 10.1016/j.adhoc.2017.09.005 10.2307/2372560 10.1109/LCOMM.2016.2626293 10.1007/s11042-018-5739-5 10.1007/978-3-319-64027-3_23 10.1007/s10845-014-0871-1 10.1109/TEVC.2009.2033578 10.1007/978-3-030-27541-9_21 10.1007/s10846-018-0961-y 10.1007/s12293-020-00304-7 10.1109/TVT.2016.2623666 10.1016/j.cie.2018.05.013 10.1016/j.ejor.2010.03.045 10.1016/j.ejor.2016.04.059 10.1109/LWC.2018.2880467 10.1007/s12293-015-0153-2 10.1016/j.cor.2017.04.011 10.1007/s11432-018-9576-x 10.1287/ijoc.1060.0186 10.1109/TCYB.2019.2932996 10.1109/TII.2012.2198665 10.1002/1520-6750(198706)34:3<307::AID-NAV3220340302>3.0.CO;2-D 10.1016/j.cor.2017.09.013 10.1016/0377-2217(94)00289-4 10.1016/j.cie.2005.01.018 10.1016/j.jclepro.2019.03.185 10.1007/s12293-016-0187-0 10.1007/s12293-019-00295-0 10.1109/ICARM49381.2020.9195287 10.1016/j.swevo.2018.06.005 10.1016/j.tre.2016.04.003 10.3390/rs10040641 10.3233/ICA-150498 10.1016/j.ast.2019.06.027 10.1016/j.cie.2019.106241 10.1007/s11432-018-9603-y 10.1016/j.cie.2015.04.011
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References	VansteenwegenPSouffriauWVan OudheusdenDThe orienteering problem: a surveyEur J Oper Res2011209111027468481205.90253 ZengZZYuXGChenMLiuYYA memetic algorithm to pack unequal circles into a squareComput Oper Res201892475537586161391.90539 YangXBostelNDejaxPA MILP model and memetic algorithm for the hub location and routing problem with distinct collection and delivery toursComput Ind Eng2019135105119 CoelhoBNCoelhoVNCoelhoIMOchiLSHaghnazarRZuidemaDLimaMSda CostaARA multi-objective green UAV routing problemComput Oper Res20178830631536870491391.90544 PengBZhangYLüZChengTGloverFA learning-based memetic algorithm for the multiple vehicle pickup and delivery problem with lifo loadingComput Ind Eng2020142106241 AtenciaCRDel SerJCamachoDWeighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planningSwarm Evolut Comput201944480495 Kim SJ, Jeong Y, Park S, Ryu K, Oh G (2018) A survey of drone use for entertainment and AVR (augmented and virtual reality). In: Augmented reality and virtual reality. Springer, pp 339–352 ManfredaSMcCabeMFMillerPELucasRPajuelo MadrigalVMallinisGBen DorEHelmanDEstesLCiraoloGOn the use of unmanned aerial systems for environmental monitoringRemote Sens2018104641 ShengWChenSShengMXiaoGMaoJZhengYAdaptive multisubpopulation competition and multiniche crowding-based memetic algorithm for automatic data clusteringIEEE Trans Evol Comput2016206838858 ZhuZXiaoJLiJQWangFZhangQGlobal path planning of wheeled robots using multi-objective memetic algorithmsIntegr Comput-Aided Eng2015224387404 Almadhoun R, Taha T, Dias J, Seneviratne L, Zweiri Y (2019) Coverage path planning for complex structures inspection using unmanned aerial vehicle (uav). In: International conference on intelligent robotics and applications. Springer, pp 243–266 LiJHanYOptimal resource allocation for packet delay minimization in multi-layer UAV networksIEEE Commun Lett2016213580583 DubinsLEOn curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangentsAm J Math1957793497516894570098.35401 WangTQinRChenYSnoussiHChoiCA reinforcement learning approach for UAV target searching and trackingMultim Tools Appl201978443474364 SongBDParkKKimJPersistent UAV delivery logistics: MILP formulation and efficient heuristicComput Ind Eng2018120418428 Ma L, Huang X, Chen J, Li J, Sun T (2020) A two-level memetic path planning algorithm for unmanned air/ground vehicle cooperative detection systems. In: 2020 5th international conference on advanced robotics and mechatronics (ICARM). IEEE, pp 25–30 GunawanALauHCVansteenwegenPOrienteering problem: a survey of recent variants, solution approaches and applicationsEur J Oper Res2016255231533235188041346.90703 HuangYChenJWangHSuGA method of 3d path planning for solar-powered UAV with fixed target and solar trackingAerosp Sci Technol201992831838 ReinaDTawfikHToralSMulti-subpopulation evolutionary algorithms for coverage deployment of UAV-networksAd Hoc Netw2018681632 GoldenBLLevyLVohraRThe orienteering problemNaval Res Logist (NRL)19873433073180647.90099 HuoMDuanHYangQZhangDQiuHLive-fly experimentation for pigeon-inspired obstacle avoidance of quadrotor unmanned aerial vehiclesSci China Inf Sci2019625522013937986 HarikumarKSenthilnathJSundaramSMulti-UAV oxyrrhis marina-inspired search and dynamic formation control for forest firefightingIEEE Trans Autom Sci Eng2018162863873 LiuXAnsariNResource allocation in UAV-assisted m2m communications for disaster rescueIEEE Wirel Commun Lett201882580583 KhosiawanYKhalfayANielsenIScheduling unmanned aerial vehicle and automated guided vehicle operations in an indoor manufacturing environment using differential evolution-fused particle swarm optimizationInt J Adv Rob Syst20181511729 LiJDengGLuoCLinQYanQMingZA hybrid path planning method in unmanned air/ground vehicle (UAV/UGV) cooperative systemsIEEE Trans Veh Technol2016651295859596 GuzeyHMDierksTJagannathanSAcarLModified consensus-based output feedback control of quadrotor UAV formations using neural networksJ Intell Robot Syst2019941283300 MaLLiJLinQGongMCoelloCACMingZCost-aware robust control of signed networks by using a memetic algorithmIEEE Trans Cybern2019501044304443 LinJZhouWWolfsonOElectric vehicle routing problemTransp Res Procedia201612508521 WuXCheAA memetic differential evolution algorithm for energy-efficient parallel machine schedulingOmega201982155165 ChaoIMGoldenBLWasilEAThe team orienteering problemEur J Oper Res19968834644740911.90145 NekkaaMBoughaciDA memetic algorithm with support vector machine for feature selection and classificationMemet Comput2015715973 EremeevAVKovalenkoYVA memetic algorithm with optimal recombination for the asymmetric travelling salesman problemMemet Comput20201212336 KachitvichyanukulVSombunthamPKunnapapdeelertSTwo solution representations for solving multi-depot vehicle routing problem with multiple pickup and delivery requests via PSOComput Ind Eng201589125136 RobergeVTarbouchiMLabontéGComparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planningIEEE Trans Ind Inf201291132141 XingLRohlfshagenPChenYYaoXAn evolutionary approach to the multidepot capacitated arc routing problemIEEE Trans Evol Comput2009143356374 ChenMCHsiaoYHReddyRHTiwariMKThe self-learning particle swarm optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docksTransp Res Part E Logist Transp Rev201691208226 da CostaPRdOMauceriSCarrollPPallonettoFA genetic algorithm for a green vehicle routing problemElectron Notes Discrete Math201864657437808891392.90019 XiaWWuZAn effective hybrid optimization approach for multi-objective flexible job-shop scheduling problemsComput Ind Eng2005482409425 GuoKLiXXieLSimultaneous cooperative relative localization and distributed formation control for multiple UAVsSci China Inf Sci20206311192014013786 LimAZhangXA two-stage heuristic with ejection pools and generalized ejection chains for the vehicle routing problem with time windowsInforms J Comput201719344345723454361241.90051 YangHMengCWangCA probability first memetic algorithm for the dynamic multiple-fault diagnosis problem with non-ideal testsMemet Comput202012101113 SammaHLimCPSalehJMSuandiSAA memetic-based fuzzy support vector machine model and its application to license plate recognitionMemet Comput201683235251 LiYSoleimaniHZohalMAn improved ant colony optimization algorithm for the multi-depot green vehicle routing problem with multiple objectivesJ Clean Prod201922711611172 ChenFWangHXieYQiCAn ACO-based online routing method for multiple order pickers with congestion consideration in warehouseJ Intell Manuf2016272389408 XueYRuiZYuXSangXLiuWEstimation of distribution evolution memetic algorithm for the unrelated parallel-machine green scheduling problemMemet Comput2019114423437 Y Xue (334_CR41) 2019; 11 Y Khosiawan (334_CR18) 2018; 15 S Manfreda (334_CR28) 2018; 10 IM Chao (334_CR3) 1996; 88 BN Coelho (334_CR6) 2017; 88 A Lim (334_CR23) 2017; 19 B Peng (334_CR30) 2020; 142 W Sheng (334_CR34) 2016; 20 Y Li (334_CR22) 2019; 227 V Kachitvichyanukul (334_CR17) 2015; 89 LE Dubins (334_CR8) 1957; 79 W Xia (334_CR39) 2005; 48 X Wu (334_CR38) 2019; 82 K Harikumar (334_CR14) 2018; 16 J Li (334_CR20) 2016; 65 K Guo (334_CR12) 2020; 63 Z Zhu (334_CR45) 2015; 22 X Liu (334_CR25) 2018; 8 334_CR1 BL Golden (334_CR10) 1987; 34 HM Guzey (334_CR13) 2019; 94 T Wang (334_CR37) 2019; 78 P Vansteenwegen (334_CR36) 2011; 209 MC Chen (334_CR5) 2016; 91 M Nekkaa (334_CR29) 2015; 7 J Lin (334_CR24) 2016; 12 H Yang (334_CR42) 2020; 12 F Chen (334_CR4) 2016; 27 A Gunawan (334_CR11) 2016; 255 334_CR26 PRdO da Costa (334_CR7) 2018; 64 ZZ Zeng (334_CR44) 2018; 92 334_CR19 X Yang (334_CR43) 2019; 135 CR Atencia (334_CR2) 2019; 44 M Huo (334_CR16) 2019; 62 BD Song (334_CR35) 2018; 120 AV Eremeev (334_CR9) 2020; 12 Y Huang (334_CR15) 2019; 92 V Roberge (334_CR32) 2012; 9 J Li (334_CR21) 2016; 21 L Xing (334_CR40) 2009; 14 H Samma (334_CR33) 2016; 8 D Reina (334_CR31) 2018; 68 L Ma (334_CR27) 2019; 50
References_xml	– reference: ReinaDTawfikHToralSMulti-subpopulation evolutionary algorithms for coverage deployment of UAV-networksAd Hoc Netw2018681632 – reference: da CostaPRdOMauceriSCarrollPPallonettoFA genetic algorithm for a green vehicle routing problemElectron Notes Discrete Math201864657437808891392.90019 – reference: LiYSoleimaniHZohalMAn improved ant colony optimization algorithm for the multi-depot green vehicle routing problem with multiple objectivesJ Clean Prod201922711611172 – reference: ManfredaSMcCabeMFMillerPELucasRPajuelo MadrigalVMallinisGBen DorEHelmanDEstesLCiraoloGOn the use of unmanned aerial systems for environmental monitoringRemote Sens2018104641 – reference: GunawanALauHCVansteenwegenPOrienteering problem: a survey of recent variants, solution approaches and applicationsEur J Oper Res2016255231533235188041346.90703 – reference: LiJHanYOptimal resource allocation for packet delay minimization in multi-layer UAV networksIEEE Commun Lett2016213580583 – reference: HuoMDuanHYangQZhangDQiuHLive-fly experimentation for pigeon-inspired obstacle avoidance of quadrotor unmanned aerial vehiclesSci China Inf Sci2019625522013937986 – reference: WangTQinRChenYSnoussiHChoiCA reinforcement learning approach for UAV target searching and trackingMultim Tools Appl201978443474364 – reference: ChenFWangHXieYQiCAn ACO-based online routing method for multiple order pickers with congestion consideration in warehouseJ Intell Manuf2016272389408 – reference: ZhuZXiaoJLiJQWangFZhangQGlobal path planning of wheeled robots using multi-objective memetic algorithmsIntegr Comput-Aided Eng2015224387404 – reference: ZengZZYuXGChenMLiuYYA memetic algorithm to pack unequal circles into a squareComput Oper Res201892475537586161391.90539 – reference: LimAZhangXA two-stage heuristic with ejection pools and generalized ejection chains for the vehicle routing problem with time windowsInforms J Comput201719344345723454361241.90051 – reference: ChenMCHsiaoYHReddyRHTiwariMKThe self-learning particle swarm optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docksTransp Res Part E Logist Transp Rev201691208226 – reference: DubinsLEOn curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangentsAm J Math1957793497516894570098.35401 – reference: LiuXAnsariNResource allocation in UAV-assisted m2m communications for disaster rescueIEEE Wirel Commun Lett201882580583 – reference: XueYRuiZYuXSangXLiuWEstimation of distribution evolution memetic algorithm for the unrelated parallel-machine green scheduling problemMemet Comput2019114423437 – reference: KachitvichyanukulVSombunthamPKunnapapdeelertSTwo solution representations for solving multi-depot vehicle routing problem with multiple pickup and delivery requests via PSOComput Ind Eng201589125136 – reference: XiaWWuZAn effective hybrid optimization approach for multi-objective flexible job-shop scheduling problemsComput Ind Eng2005482409425 – reference: NekkaaMBoughaciDA memetic algorithm with support vector machine for feature selection and classificationMemet Comput2015715973 – reference: HuangYChenJWangHSuGA method of 3d path planning for solar-powered UAV with fixed target and solar trackingAerosp Sci Technol201992831838 – reference: XingLRohlfshagenPChenYYaoXAn evolutionary approach to the multidepot capacitated arc routing problemIEEE Trans Evol Comput2009143356374 – reference: YangHMengCWangCA probability first memetic algorithm for the dynamic multiple-fault diagnosis problem with non-ideal testsMemet Comput202012101113 – reference: CoelhoBNCoelhoVNCoelhoIMOchiLSHaghnazarRZuidemaDLimaMSda CostaARA multi-objective green UAV routing problemComput Oper Res20178830631536870491391.90544 – reference: HarikumarKSenthilnathJSundaramSMulti-UAV oxyrrhis marina-inspired search and dynamic formation control for forest firefightingIEEE Trans Autom Sci Eng2018162863873 – reference: ShengWChenSShengMXiaoGMaoJZhengYAdaptive multisubpopulation competition and multiniche crowding-based memetic algorithm for automatic data clusteringIEEE Trans Evol Comput2016206838858 – reference: WuXCheAA memetic differential evolution algorithm for energy-efficient parallel machine schedulingOmega201982155165 – reference: SammaHLimCPSalehJMSuandiSAA memetic-based fuzzy support vector machine model and its application to license plate recognitionMemet Comput201683235251 – reference: Kim SJ, Jeong Y, Park S, Ryu K, Oh G (2018) A survey of drone use for entertainment and AVR (augmented and virtual reality). In: Augmented reality and virtual reality. Springer, pp 339–352 – reference: VansteenwegenPSouffriauWVan OudheusdenDThe orienteering problem: a surveyEur J Oper Res2011209111027468481205.90253 – reference: GuzeyHMDierksTJagannathanSAcarLModified consensus-based output feedback control of quadrotor UAV formations using neural networksJ Intell Robot Syst2019941283300 – reference: Ma L, Huang X, Chen J, Li J, Sun T (2020) A two-level memetic path planning algorithm for unmanned air/ground vehicle cooperative detection systems. In: 2020 5th international conference on advanced robotics and mechatronics (ICARM). IEEE, pp 25–30 – reference: KhosiawanYKhalfayANielsenIScheduling unmanned aerial vehicle and automated guided vehicle operations in an indoor manufacturing environment using differential evolution-fused particle swarm optimizationInt J Adv Rob Syst20181511729 – reference: EremeevAVKovalenkoYVA memetic algorithm with optimal recombination for the asymmetric travelling salesman problemMemet Comput20201212336 – reference: GoldenBLLevyLVohraRThe orienteering problemNaval Res Logist (NRL)19873433073180647.90099 – reference: RobergeVTarbouchiMLabontéGComparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planningIEEE Trans Ind Inf201291132141 – reference: SongBDParkKKimJPersistent UAV delivery logistics: MILP formulation and efficient heuristicComput Ind Eng2018120418428 – reference: AtenciaCRDel SerJCamachoDWeighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planningSwarm Evolut Comput201944480495 – reference: LiJDengGLuoCLinQYanQMingZA hybrid path planning method in unmanned air/ground vehicle (UAV/UGV) cooperative systemsIEEE Trans Veh Technol2016651295859596 – reference: YangXBostelNDejaxPA MILP model and memetic algorithm for the hub location and routing problem with distinct collection and delivery toursComput Ind Eng2019135105119 – reference: PengBZhangYLüZChengTGloverFA learning-based memetic algorithm for the multiple vehicle pickup and delivery problem with lifo loadingComput Ind Eng2020142106241 – reference: Almadhoun R, Taha T, Dias J, Seneviratne L, Zweiri Y (2019) Coverage path planning for complex structures inspection using unmanned aerial vehicle (uav). In: International conference on intelligent robotics and applications. Springer, pp 243–266 – reference: GuoKLiXXieLSimultaneous cooperative relative localization and distributed formation control for multiple UAVsSci China Inf Sci20206311192014013786 – reference: LinJZhouWWolfsonOElectric vehicle routing problemTransp Res Procedia201612508521 – reference: ChaoIMGoldenBLWasilEAThe team orienteering problemEur J Oper Res19968834644740911.90145 – reference: MaLLiJLinQGongMCoelloCACMingZCost-aware robust control of signed networks by using a memetic algorithmIEEE Trans Cybern2019501044304443 – volume: 12 start-page: 23 issue: 1 year: 2020 ident: 334_CR9 publication-title: Memet Comput doi: 10.1007/s12293-019-00291-4 – volume: 12 start-page: 508 year: 2016 ident: 334_CR24 publication-title: Transp Res Procedia doi: 10.1016/j.trpro.2016.02.007 – volume: 135 start-page: 105 year: 2019 ident: 334_CR43 publication-title: Comput Ind Eng doi: 10.1016/j.cie.2019.05.038 – volume: 64 start-page: 65 year: 2018 ident: 334_CR7 publication-title: Electron Notes Discrete Math doi: 10.1016/j.endm.2018.01.008 – volume: 16 start-page: 863 issue: 2 year: 2018 ident: 334_CR14 publication-title: IEEE Trans Autom Sci Eng doi: 10.1109/TASE.2018.2867614 – volume: 15 start-page: 17 issue: 1 year: 2018 ident: 334_CR18 publication-title: Int J Adv Rob Syst – volume: 82 start-page: 155 year: 2019 ident: 334_CR38 publication-title: Omega doi: 10.1016/j.omega.2018.01.001 – volume: 68 start-page: 16 year: 2018 ident: 334_CR31 publication-title: Ad Hoc Netw doi: 10.1016/j.adhoc.2017.09.005 – volume: 79 start-page: 497 issue: 3 year: 1957 ident: 334_CR8 publication-title: Am J Math doi: 10.2307/2372560 – volume: 21 start-page: 580 issue: 3 year: 2016 ident: 334_CR21 publication-title: IEEE Commun Lett doi: 10.1109/LCOMM.2016.2626293 – volume: 78 start-page: 4347 issue: 4 year: 2019 ident: 334_CR37 publication-title: Multim Tools Appl doi: 10.1007/s11042-018-5739-5 – ident: 334_CR19 doi: 10.1007/978-3-319-64027-3_23 – volume: 27 start-page: 389 issue: 2 year: 2016 ident: 334_CR4 publication-title: J Intell Manuf doi: 10.1007/s10845-014-0871-1 – volume: 20 start-page: 838 issue: 6 year: 2016 ident: 334_CR34 publication-title: IEEE Trans Evol Comput – volume: 14 start-page: 356 issue: 3 year: 2009 ident: 334_CR40 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2009.2033578 – ident: 334_CR1 doi: 10.1007/978-3-030-27541-9_21 – volume: 94 start-page: 283 issue: 1 year: 2019 ident: 334_CR13 publication-title: J Intell Robot Syst doi: 10.1007/s10846-018-0961-y – volume: 12 start-page: 101 year: 2020 ident: 334_CR42 publication-title: Memet Comput doi: 10.1007/s12293-020-00304-7 – volume: 65 start-page: 9585 issue: 12 year: 2016 ident: 334_CR20 publication-title: IEEE Trans Veh Technol doi: 10.1109/TVT.2016.2623666 – volume: 120 start-page: 418 year: 2018 ident: 334_CR35 publication-title: Comput Ind Eng doi: 10.1016/j.cie.2018.05.013 – volume: 209 start-page: 1 issue: 1 year: 2011 ident: 334_CR36 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2010.03.045 – volume: 255 start-page: 315 issue: 2 year: 2016 ident: 334_CR11 publication-title: Eur J Oper Res doi: 10.1016/j.ejor.2016.04.059 – volume: 8 start-page: 580 issue: 2 year: 2018 ident: 334_CR25 publication-title: IEEE Wirel Commun Lett doi: 10.1109/LWC.2018.2880467 – volume: 7 start-page: 59 issue: 1 year: 2015 ident: 334_CR29 publication-title: Memet Comput doi: 10.1007/s12293-015-0153-2 – volume: 88 start-page: 306 year: 2017 ident: 334_CR6 publication-title: Comput Oper Res doi: 10.1016/j.cor.2017.04.011 – volume: 62 start-page: 52201 issue: 5 year: 2019 ident: 334_CR16 publication-title: Sci China Inf Sci doi: 10.1007/s11432-018-9576-x – volume: 19 start-page: 443 issue: 3 year: 2017 ident: 334_CR23 publication-title: Informs J Comput doi: 10.1287/ijoc.1060.0186 – volume: 50 start-page: 4430 issue: 10 year: 2019 ident: 334_CR27 publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2019.2932996 – volume: 9 start-page: 132 issue: 1 year: 2012 ident: 334_CR32 publication-title: IEEE Trans Ind Inf doi: 10.1109/TII.2012.2198665 – volume: 34 start-page: 307 issue: 3 year: 1987 ident: 334_CR10 publication-title: Naval Res Logist (NRL) doi: 10.1002/1520-6750(198706)34:3<307::AID-NAV3220340302>3.0.CO;2-D – volume: 92 start-page: 47 year: 2018 ident: 334_CR44 publication-title: Comput Oper Res doi: 10.1016/j.cor.2017.09.013 – volume: 88 start-page: 464 issue: 3 year: 1996 ident: 334_CR3 publication-title: Eur J Oper Res doi: 10.1016/0377-2217(94)00289-4 – volume: 48 start-page: 409 issue: 2 year: 2005 ident: 334_CR39 publication-title: Comput Ind Eng doi: 10.1016/j.cie.2005.01.018 – volume: 227 start-page: 1161 year: 2019 ident: 334_CR22 publication-title: J Clean Prod doi: 10.1016/j.jclepro.2019.03.185 – volume: 8 start-page: 235 issue: 3 year: 2016 ident: 334_CR33 publication-title: Memet Comput doi: 10.1007/s12293-016-0187-0 – volume: 11 start-page: 423 issue: 4 year: 2019 ident: 334_CR41 publication-title: Memet Comput doi: 10.1007/s12293-019-00295-0 – ident: 334_CR26 doi: 10.1109/ICARM49381.2020.9195287 – volume: 44 start-page: 480 year: 2019 ident: 334_CR2 publication-title: Swarm Evolut Comput doi: 10.1016/j.swevo.2018.06.005 – volume: 91 start-page: 208 year: 2016 ident: 334_CR5 publication-title: Transp Res Part E Logist Transp Rev doi: 10.1016/j.tre.2016.04.003 – volume: 10 start-page: 641 issue: 4 year: 2018 ident: 334_CR28 publication-title: Remote Sens doi: 10.3390/rs10040641 – volume: 22 start-page: 387 issue: 4 year: 2015 ident: 334_CR45 publication-title: Integr Comput-Aided Eng doi: 10.3233/ICA-150498 – volume: 92 start-page: 831 year: 2019 ident: 334_CR15 publication-title: Aerosp Sci Technol doi: 10.1016/j.ast.2019.06.027 – volume: 142 start-page: 106241 year: 2020 ident: 334_CR30 publication-title: Comput Ind Eng doi: 10.1016/j.cie.2019.106241 – volume: 63 start-page: 119201 issue: 1 year: 2020 ident: 334_CR12 publication-title: Sci China Inf Sci doi: 10.1007/s11432-018-9603-y – volume: 89 start-page: 125 year: 2015 ident: 334_CR17 publication-title: Comput Ind Eng doi: 10.1016/j.cie.2015.04.011
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Snippet	Navigation correction is usually frequently required by unmanned aerial vehicles (UAVs), especially under uncertain navigation environments. Although the UAV’s...
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SubjectTerms	Algorithms Applications of Mathematics Artificial Intelligence Bioinformatics Complex Systems Control Engineering Heuristic methods Knowledge bases (artificial intelligence) Mathematical and Computational Engineering Mechatronics Navigation Optimization Regular Research Paper Robotics Steering Unmanned aerial vehicles
Title	An effective memetic algorithm for UAV routing and orientation under uncertain navigation environments
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