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All issues Volume 60 / No 1 (January-February 2026) RAIRO-Oper. Res., 60 1 (2026) 281-305 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 60, Number 1, January-February 2026
Page(s) 281 - 305
DOI https://doi.org/10.1051/ro/2025159
Published online 18 March 2026
  • H. Xu, Z. Niu, B. Jiang, Y. Zhang, S. Chen, Z. Li, M. Gao and M. Zhu, ERRT-GA: expert genetic algorithm with rapidly exploring random tree initialization for UAV path planning. Drones 8 (2023) 367. [Google Scholar]
  • S. Ghambari, L. Idoumghar, L. Jourdan and J. Lepagnot, A hybrid evolutionary algorithm for offline UAV path planning, in International Conference on Artificial Evolution (Evolution Artificielle). Vol. 12052. Springer, Cham (2020) 205–218. [Google Scholar]
  • M.M. Quamar, B. Al-Ramadan, K. Khan, M. Shafiullah and S. El Ferik, Advancements and applications of drone-integrated geographic information system technology a review. Remote Sensing 15 (2023) 5039. [CrossRef] [Google Scholar]
  • V.T. Hoang, M.D. Phung, T.H. Dinh and Q.P. Ha, System architecture for realtime surface inspection using multiple UAVs. IEEE Syst. J. 14 (2020) 2925–2936. [Google Scholar]
  • M. Duong Phung and Q.P. Ha, Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization. Appl. Soft Comput. 107 (2021) 107376. [Google Scholar]
  • X. Yu and W. Luo, Reinforcement learning-based multi-strategy cuckoo search for 3D UAV path planning. Expert Syst. App. 223 (2023) 119910. [Google Scholar]
  • M.A. Gutierrez-Martinez, E.G. Rojo-Rodriguez, L.E. Cabriales-Ramirez and K. Estabridis, Genetic algorithm-based path planning of quadrotor UAVs in a 3D environment. Aeronautical J. 129 (2025) 902–938. [Google Scholar]
  • M. Zhang, Y. Han, S. Chen, M. Liu, Z. He and N. Pan, A multi-strategy improved differential evolution algorithm for UAV path planning in mountainous environments. Eng. App. Artif. Intell. 125 (2023) 106672. [Google Scholar]
  • H. Chen, Y. Liang and X. Meng, A UAV path planning method for building surface information acquisition utilizing opposition-based learning artificial bee colony algorithm. Remote Sensing 15 (2023) 4312. [Google Scholar]
  • X. Mai, N. Dong, S. Liu and H. Chen, UAV path planning based on a dual-strategy ant colony optimization algorithm. Intell. Rob. 3 (2023) 666–684. [Google Scholar]
  • Q. Cheng, Z. Zhang, Y. Du and Y. Li, Research on particle swarm optimization-based UAV path planning technology in urban airspace. Drones 8 (2024) 701. [Google Scholar]
  • X. Liu, X. Du, X. Zhang, Q. Zhu and M. Guizani, Evolution-algorithm-based unmanned aerial vehicles path planning in complex environment. Comput. Electr. Eng. 80 (2019) 106493. [Google Scholar]
  • Z. Wang, H. Gong, M. Nie and X. Liu, Research on multi-UAV cooperative dynamic path planning algorithm based on conflict search. Drones 8 (2024) 274. [Google Scholar]
  • S. Brotee, F. Kabir, M.A. Razzaque, P. Roy, M. Mamun-Or-Rashid, M.R. Hassan and M.M. Hassan, Optimizing UAV-UGV coalition operations: a hybrid clustering and multi-agent reinforcement learning approach for path planning in obstructed environments. Ad Hoc Networks 160 (2024) 103519. [Google Scholar]
  • J. Westheider, J. Rückin and M. Popoviæ, Multi-UAV adaptive path planning using deep reinforcement learning, in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2023). [Google Scholar]
  • A. Shamsoshoara, F. Lotfi, S. Mousavi, F. Afghah and I Güvenç, Joint path planning and power allocation of a cellular-connected UAV using apprenticeship learning via deep inverse reinforcement learning. Comput. Networks 254 (2024) 110789. [Google Scholar]
  • Y. Fu, M. Ding, C. Zhou and H. Hu, Route planning for unmanned aerial vehicle (UAV) on the sea using hybrid differential evolution and quantum-behaved particle swarm optimization. IEEE Trans. Syst. Man Cybern. Syst. 43 (2013) 1451–1465. [Google Scholar]
  • Y. Zhang, D. Gong and J. Zhang, Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing 103 (2013) 172–185. [Google Scholar]
  • Y. Jiang, X.X. Xu, M.-Y. Zheng and Z.-H. Zhan, Evolutionary computation for unmanned aerial vehicle path planning. Artif. Intell. Rev. 57 (2024) 267. [Google Scholar]
  • S. Xia and X. Zhang, Constrained path planning for unmanned aerial vehicle in 3D terrain using modified multi-objective particle swarm optimization. Actuators 10 (2021) 255. [Google Scholar]
  • T. Liu, L. Jiao, W. Ma, J. Ma and R. Shang, A new quantum-behaved particle swarm optimization based on cultural evolution mechanism for multiobjective problems. Knowl.-Based Syst. 101 (2016) 90–99. [Google Scholar]
  • C.A.C. Coello and M.S. Lechuga, MOPSO: a proposal for multiple objective particle swarm optimization, in Congress on Evolutionary Computation. CEC'02. IEEE (2002) 1051–1056. [Google Scholar]
  • M.Z. bin Mohd Zain, J. Kanesan, J.H. Chuah, S. Dhanapal and G. Kendall, A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization. Appl. Soft Comput. 70 (2018) 680–700. [Google Scholar]
  • X. Chen, J. Fang and Y. Zhai, UAV path planning method considering safety and signal shielding risk. Int. J. Adv. Comput. Sci. App. 15 (2024) 980. [Google Scholar]
  • A. Majeed and S.O. Hwang, A multi-objective coverage path planning algorithm for UAVs to cover spatially dis-tributed regions in urban environments. Aerospace 8 (2021) 343. [Google Scholar]
  • J. Chen, R. Zhang, H. Zhao, J. Li and J. He, Path planning of multiple unmanned aerial vehicles covering multiple regions based on minimum consumption ratio. Aerospace 10 (2023) 93. [Google Scholar]
  • ?.?. Mac, C. Copot, D.T. Tran and R. De Keyser, A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. Appl. Soft Comput. 59 (2017) 68–76. [Google Scholar]
  • X. Zhang, S. Xia, X. Li and T. Zhang, Multi-objective particle swarm optimization with a multi-mode collaboration based on reinforcement learning for path planning of unmanned air vehicles. Knowl.-Based Syst. 250 (2022) 109075. [Google Scholar]
  • B. Tong, L. Chen and H. Duan, A path planning method for UAVs based on multi-objective pigeon-inspired opti-mization and differential evolution. Int. J. Bio-Inspired Comput. 17 (2021) 105–112. [Google Scholar]
  • M. Golabi, S. Ghambari, J. Lepagnot, L. Jourdan, M. Brévilliers and L. Idoumghar, Bypassing or flying above the obstacles? A novel multi-objective UAV path planning problem, in IEEE Congress on Evolutionary Computation (CEC). IEEE (2020). [Google Scholar]
  • X.U. Zhen, Z. Enze and C. Qingwei, Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization. J. Syst. Eng. Electron. 31 (2020) 130–141. [Google Scholar]
  • G. Airlangga, R. Sukwadi, W.W. Basuki, L.F. Sugianto, O.I.A. Nugroho, Y. Kristian and R. Rahmananta, Adaptive path planning for multi-UAV systems in dynamic 3D environments: a multi-objective framework. Designs 8 (2024) 136. [Google Scholar]
  • Z.-J. Teng, J.-L. Lv and L.-W. Guo, An improved hybrid grey wolf optimization algorithm. Soft Comput. 23 (2019) 6617–6631. [CrossRef] [Google Scholar]
  • W. Hao, C. Wu and S. Lin, Research on UAV path planning based on improved particle swarm algorithm with inertia weight, in 2023 IEEE International Conference on Control, Electronics and Computer Technology (ICCECT). IEEE (2023) 738–741. [Google Scholar]
  • S. Poudel and S. Moh, Hybrid path planning for efficient data collection in UAV-aided WSNs for emergency appli-cations. Sensors 21 (2021) 2839. [CrossRef] [PubMed] [Google Scholar]
  • Y. He, T. Hou and M. Wang, A new method for unmanned aerial vehicle path planning in complex environments. Sci. Rep. 14 (2024) 9257. [Google Scholar]
  • C.A.C. Coello, G.T. Pulido and M.S. Lechuga, Handling multiple objectives with particle swarm optimization. IEEE Trans. Evol. Comput. 8 (2004) 256–279. [CrossRef] [Google Scholar]
  • T.T.N. Duong, D.N. Bui and M.D. Phung, Navigation variable-based multi-objective particle swarm optimization for UAV path planning with kinematic constraints. Neural Comput. App. 37 (2025) 5683–5697. [Google Scholar]
  • X. Zhang, S. Xia, X. Li and T. Zhang, Multi-objective particle swarm optimization with multi-mode collaboration based on reinforcement learning for path planning of unmanned air vehicles. Knowl.-Based Syst. 250 (2022) 109075. [Google Scholar]
  • Y. Hu, Y. Zhang and D. Gong, Multiobjective particle swarm optimization for feature selection with fuzzy cost. IEEE Trans. Cybern. 51 (2020) 874–888. [Google Scholar]
  • M.A. Alzaqebah, S. Jawarneh, R. Mustafa, A. Mohammad and M.K. Alsmadi, Hybrid feature selection method based on particle swarm optimization and adaptive local search method. Int. J. Electr. Comput. Eng. (IJECE) 11 (2020). [Google Scholar]
  • X. Liu, C. Wang, S. Jiang, Y. Gao, Chaomurilige and B. Cheng, Multi-objective cauchy particle swarm optimization for energy-aware virtual machine placement in cloud datacenters. Symmetry 17 (2025) 742. [Google Scholar]
  • X. Chen and S. Xiao, Multi-objective and parallel particle swarm optimization algorithm for container-based microservice scheduling. Sensors 21 (2021) 6212. [Google Scholar]
  • A. Zheng, Z. Zhang, W. Liu, J. Liu, Y. Xiao and C. Li, Dual cluster head optimization of wireless sensor networks based on MOPSO. Sensors 23 (2023) 231. [Google Scholar]
  • Y. Tong, L. Lin, L. Tian, Z. Wang, W. Wu and J. Wu, Coverage optimization and node minimization in WSNs: an enhanced hybrid PSO approach with spatial position encoding. Sci. Rep. 15 (2025) 25332. [Google Scholar]
  • Y. Zhang, L. Yang and Y. Tan, Energy-efficient adaptive routing in heterogeneous wireless sensor networks via hybrid PSO and dynamic clustering. J. Cloud Comput. 14 (2025) 46. [Google Scholar]
  • E.J.R. Freitas, M.W. Cohen, F.G. Guimarães and L.C.A. Pimenta, Online path planning for kinematic-constrained UAVs in a dynamic environment based on a differential evolution algorithm. Preprint arXiv: 2410.18777 (2024). [Google Scholar]
  • R.T. Marler and J.S. Arora, The weighted sum method for multi-objective optimization: new insights. Struct. Multidiscipl. Optim. 41 (2010) 853–862. [Google Scholar]
  • A. Williams and Y. Cai, Insights into weighted sum sampling approaches for multi-criteria decision making problems. Preprint arXiv: 2410.03931 (2024). [Google Scholar]
  • G. Zhu, M. Ye, X. Yu, J. Liu, M. Wang, Z. Luo, H. Liang and Y. Zhong, Optimizing route planning via the weighted sum method and multi-criteria decision-making. Mathematics 13 (2025) 1704. [Google Scholar]
  • A.M. Sultan and A.B. Templeman, Generation of pareto solutions by entropy-based methods, in Multi-Objective Programming and Goal Programming. Springer (1996) 164–195. [Google Scholar]
  • V.S. Rugveth and K. Khatter, Sensitivity analysis on Gaussian quantum-behaved particle swarm optimization control parameters. Soft Comput. 27 (2023) 8759–8774. [Google Scholar]
  • A.J. Nebro, M. López-Ibáñez, J. García-Nieto and C.A. Coello Coello, On the automatic design of multi-objective particle swarm optimizers: experimentation and analysis. Swarm Intell. 18 (2024) 105–139. [Google Scholar]
  • Y. Fu, M. Ding and C. Zhou, Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 42 (2012) 511–526. [CrossRef] [Google Scholar]
  • H. Wang, L. Tan, J. Shi, X. Lv and X. Lian, An enhanced NSGA-II algorithm for UAV path planning problems. J. Internet. Technol. 22 (2021) 583–592. [Google Scholar]
  • D. Adhikari, E. Kim and H. Reza, A fuzzy adaptive differential evolution for multi-objective 3D UAV path planning, in IEEE Congress on Evolutionary Computation (CEC), IEEE (2017) 2258–2265. [Google Scholar]
  • E. Zitzler, M. Laumanns and L. Thiele, SPEA2: improving the strength Pareto evolutionary algorithm. Technical report TIK-103, ETH Zurich, 2001. [Google Scholar]
  • X. Wang, Y. Feng, J. Tang, Z. Dai and W. Zhao, A UAV path planning method based on the framework of multi-objective jellyfish search algorithm. Sci. Rep. 14 (2024) 28058. [Google Scholar]
  • Y. Xiao, H. Yang, H. Liu, K. Wu and G. Wu, AAV 3-D path planning based on MOEA/D with adaptive areal weight adjustment. IEEE Trans. Aerospace Electron. Syst. 61 (2025) 753–769. [Google Scholar]

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