Mykola Nikolaiev; Mykhailo Novotarskyi; Artem Volokyta

A Novel Hybrid Differential Evolution and Enhanced Whale Optimization Algorithm for UAV Path Planning

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Author(s)

Mykola Nikolaiev ¹ Mykhailo Novotarskyi ² Artem Volokyta ^1,*

1. Department of computer engineering, National Technical University of Ukraine, “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv, 03056, Ukraine

2. Department of computer engineering, National Technical University of Ukraine, “Igor Sikorsky Kyiv Polytechnic Institute”/ Kyiv, 03056, Ukraine

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2025.06.06

Received: 24 Jun. 2025 / Revised: 6 Sep. 2025 / Accepted: 2 Oct. 2025 / Published: 8 Dec. 2025

Index Terms

UAV, Path Planning, Differential Evolution, Whale Optimization Algorithm, Evolutionary Computation, Autonomous Flight Optimization, Optimization

Abstract

Safe and energy-aware navigation for unmanned aerial vehicles (UAVs) requires the simultaneous optimization of path length, curvature, obstacle clearance, altitude, energy expenditure, and mission time—within the tight computational limits of on-board processors. This study proposes a two-phase hybrid optimizer that couples the global search capability of Differential Evolution (DE) with an Enhanced Whale Optimization Algorithm (E-WOA) specialized for local refinement. E-WOA improves on the canonical WOA through three principled modifications: real-time boundary repair to ensure path feasibility, quasi-oppositional learning to restore population diversity, and an adaptive stagnation trigger that re-initiates exploration when progress stalls. When the population’s improvement plateaus, control transfers from DE to E-WOA, combining broad exploration with focused exploitation. Comparative experiments conducted in 3D environments with static obstacles that block direct line-of-sight routes demonstrate that the hybrid achieves lower composite cost—normalized over path length, curvature, risk, altitude, energy and time—shorter and smoother trajectories, and faster convergence than standard metaheuristics while preserving obstacle clearances and curvature limits. Averaged over 30 independent trials, our hybrid framework reduced the normalized composite cost by 14.5% relative to the next-best algorithm (Grey Wolf Optimizer) and produced feasible paths in an average of 2.35 seconds on commodity hardware—adequate for strategic re-planning, though further optimization is needed for sub-second control loops. Blending DE’s global reach with a diversity-aware, adaptively stalled WOA provides a practical foundation for strategic, near-real-time replanning in 3D airspaces.

Cite This Paper

Mykola Nikolaiev, Mykhailo Novotarskyi, Artem Volokyta, "A Novel Hybrid Differential Evolution and Enhanced Whale Optimization Algorithm for UAV Path Planning", International Journal of Information Technology and Computer Science(IJITCS), Vol.17, No.6, pp.109-126, 2025. DOI:10.5815/ijitcs.2025.06.06

Reference

[1]S. Yin, J. Yang, L. Ma, M. Fu, and K. Xu, “An Enhanced Whale Algorithm for Three-Dimensional Path Planning for Meteorological Detection of the Unmanned Aerial Vehicle in Complex Environments,” IEEE Access, vol. 12. Institute of Electrical and Electronics Engineers (IEEE), pp. 60039–60057, 2024. doi:10.1109/access.2024.3394055.
[2]Y. Li, A. Gao, H. Li, and L. Li, “An improved whale optimization algorithm for UAV swarm trajectory planning,” Advances in Continuous and Discrete Models, vol. 2024, no. 1. Springer Science and Business Media LLC, Sep. 26, 2024. doi:10.1186/s13662-024-03841-4.
[3]Q. Wu, W. Tan, R. Zhan, L. Jiang, L. Zhu, and H. Wu, “GLBWOA: A Global–Local Balanced Whale Optimization Algorithm for UAV Path Planning,” Electronics, vol. 13, no. 23. MDPI AG, p. 4598, Nov. 21, 2024. doi:10.3390/electronics13234598.
[4]Y. Dai, J. Yu, C. Zhang, B. Zhan, and X. Zheng, “A novel whale optimization algorithm of path planning strategy for mobile robots,” Applied Intelligence, vol. 53, no. 9. Springer Science and Business Media LLC, pp. 10843–10857, Aug. 26, 2022. doi:10.1007/s10489-022-04030-0.
[5]H. Liu, Q. Chen, N. Pan, Y. Sun, Y. An, and D. Pan, “UAV Stocktaking Task-Planning for Industrial Warehouses Based on the Improved Hybrid Differential Evolution Algorithm,” IEEE Transactions on Industrial Informatics, vol. 18, no. 1. Institute of Electrical and Electronics Engineers (IEEE), pp. 582–591, Jan. 2022. doi:10.1109/tii.2021.3054172.
[6]G. Hu, F. Huang, A. Seyyedabbasi, and G. Wei, “Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning,” Applied Mathematical Modelling, vol. 130. Elsevier BV, pp. 243–271, Jun. 2024. doi:10.1016/j.apm.2024.03.001.
[7]Y. Zhang, Y. Zhu, H. Li, and J. Wang, “A hybrid optimization algorithm for multi-agent dynamic planning with guaranteed convergence in probability,” Neurocomputing, vol. 592. Elsevier BV, p. 127764, Aug. 2024. doi:10.1016/j.neucom.2024.127764.
[8]X. Zhang, L. Yang, Y. Li, W. Yang, and Y. Shi, “A nonlinear weighted whale optimization algorithm improved by cosine function for UAV path planning,” IET Conference Proceedings, vol. 2024, no. 13. Institution of Engineering and Technology (IET), pp. 1001–1007, Jan. 2025. doi:10.1049/icp.2024.3022.
[9]M. Nikolaiev and M. Novotarskyi, “Comparative Review of Drone Simulators,” Information, Computing and Intelligent systems, no. 4. Igor Sikorsky Kyiv Polytechnic Institute, pp. 79–98, Oct. 02, 2024. doi:10.20535/2786-8729.4.2024.300614.
[10]Z. Zhang, J. Zhu, and F. Nie, “A novel hybrid adaptive differential evolution for global optimization,” Scientific Reports, vol. 14, no. 1. Springer Science and Business Media LLC, Aug. 24, 2024. doi:10.1038/s41598-024-70731-w.
[11]M. Hooshyar and Y.-M. Huang, “Meta-heuristic Algorithms in UAV Path Planning Optimization: A Systematic Review (2018–2022),” Drones, vol. 7, no. 12. MDPI AG, p. 687, Nov. 25, 2023. doi:10.3390/drones7120687.
[12]G. Sun, Y. Shang, K. Yuan, and H. Gao, “An Improved Whale Optimization Algorithm Based on Nonlinear Parameters and Feedback Mechanism,” International Journal of Computational Intelligence Systems, vol. 15, no. 1. Springer Science and Business Media LLC, Jun. 10, 2022. doi:10.1007/s44196-022-00092-7.
[13]S. Fu, K. Li, H. Huang, C. Ma, Q. Fan, and Y. Zhu, “Red-billed blue magpie optimizer: a novel metaheuristic algorithm for 2D/3D UAV path planning and engineering design problems,” Artificial Intelligence Review, vol. 57, no. 6. Springer Science and Business Media LLC, May 03, 2024. doi:10.1007/s10462-024-10716-3.
[14]S. Aggarwal and N. Kumar, “Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges,” Computer Communications, vol. 149. Elsevier BV, pp. 270–299, Jan. 2020. doi:10.1016/j.comcom.2019.10.014.
[15]Y. Zhao, Z. Zheng, and Y. Liu, “Survey on computational-intelligence-based UAV path planning,” Knowledge-Based Systems, vol. 158. Elsevier BV, pp. 54–64, Oct. 2018. doi:10.1016/j.knosys.2018.05.033.
[16]J. Tisdale, Z. Kim, and J. Hedrick, “Autonomous UAV path planning and estimation,” IEEE Robotics & Automation Magazine, vol. 16, no. 2. Institute of Electrical and Electronics Engineers (IEEE), pp. 35–42, Jun. 2009. doi:10.1109/mra.2009.932529.
[17]D. Debnath, F. Vanegas, J. Sandino, A. F. Hawary, and F. Gonzalez, “A Review of UAV Path-Planning Algorithms and Obstacle Avoidance Methods for Remote Sensing Applications,” Remote Sensing, vol. 16, no. 21. MDPI AG, p. 4019, Oct. 29, 2024. doi:10.3390/rs16214019.
[18]W. Zhang, S. Zhang, F. Wu, and Y. Wang, “Path Planning of UAV Based on Improved Adaptive Grey Wolf Optimization Algorithm,” IEEE Access, vol. 9. Institute of Electrical and Electronics Engineers (IEEE), pp. 89400–89411, 2021. doi:10.1109/access.2021.3090776.
[19]Z. Qadir, M. H. Zafar, S. K. R. Moosavi, K. N. Le, and M. A. P. Mahmud, “Autonomous UAV Path-Planning Optimization Using Metaheuristic Approach for Predisaster Assessment,” IEEE Internet of Things Journal, vol. 9, no. 14. Institute of Electrical and Electronics Engineers (IEEE), pp. 12505–12514, Jul. 15, 2022. doi:10.1109/jiot.2021.3137331.
[20]L. Zhou, Z. Luo, P. Jiao, X. Jin, X. Sun, and J. Shi, “A Priority-Based Hybrid Algorithm for UAV Path Planning,” 2024 10th International Conference on Big Data and Information Analytics (BigDIA). IEEE, pp. 518–523, Oct. 25, 2024. doi:10.1109/bigdia63733.2024.10808417.
[21]Z. Zhang, J. Jiang, J. Wu, and X. Zhu, “Efficient and optimal penetration path planning for stealth unmanned aerial vehicle using minimal radar cross-section tactics and modified A-Star algorithm,” ISA Transactions, vol. 134. Elsevier BV, pp. 42–57, Mar. 2023. doi:10.1016/j.isatra.2022.07.032.
[22]R. Zhen, Q. Gu, Z. Shi, and Y. Suo, “An Improved A-Star Ship Path-Planning Algorithm Considering Current, Water Depth, and Traffic Separation Rules,” Journal of Marine Science and Engineering, vol. 11, no. 7. MDPI AG, p. 1439, Jul. 18, 2023. doi:10.3390/jmse11071439.
[23]K. Hao, Y. Yang, Z. Li, Y. Liu, and X. Zhao, “CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments,” Applied Sciences, vol. 13, no. 17. MDPI AG, p. 9666, Aug. 26, 2023. doi:10.3390/app13179666.
[24]P. Xin, X. Wang, X. Liu, Y. Wang, Z. Zhai, and X. Ma, “Improved Bidirectional RRT* Algorithm for Robot Path Planning,” Sensors, vol. 23, no. 2. MDPI AG, p. 1041, Jan. 16, 2023. doi:10.3390/s23021041.
[25]W. Zhao, L. Li, Y. Wang, H. Zhan, Y. Fu, and Y. Song, “Research on A Global Path-Planning Algorithm for Unmanned Arial Vehicle Swarm in Three-Dimensional Space Based on Theta*–Artificial Potential Field Method,” Drones, vol. 8, no. 4. MDPI AG, p. 125, Mar. 27, 2024. doi:10.3390/drones8040125.
[26] Y. Chen, G. Luo, Y. Mei, J. Yu, and X. Su, “UAV path planning using artificial potential field method updated by optimal control theory,” International Journal of Systems Science, vol. 47, no. 6. Informa UK Limited, pp. 1407–1420, Jun. 18, 2014. doi:10.1080/00207721.2014.929191.
[27]K. Sreelakshmy, H. Gupta, I. A. Ansari, S. Sharma, K. K. Goyal, and O. P. Verma, “Metaheuristic Optimization for Three Dimensional Path Planning of UAV,” Lecture Notes in Networks and Systems. Springer Nature Singapore, pp. 791–802, 2022. doi:10.1007/978-981-19-0707-4_71.
[28]M. S. Adam, N. F. Abdullah, A. Abu-Samah, O. A. Amodu, and R. Nordin, “Advanced Path Planning for UAV Swarms in Smart City Disaster Scenarios Using Hybrid Metaheuristic Algorithms,” Drones, vol. 9, no. 1. MDPI AG, p. 64, Jan. 16, 2025. doi:10.3390/drones9010064.
[29]E. Baş and E. Ülker, “Improved social spider algorithm for large scale optimization,” Artificial Intelligence Review, vol. 54, no. 5. Springer Science and Business Media LLC, pp. 3539–3574, Nov. 09, 2020. doi:10.1007/s10462-020-09931-5.
[30]X. Yu, N. Jiang, X. Wang, and M. Li, “A hybrid algorithm based on grey wolf optimizer and differential evolution for UAV path planning,” Expert Systems with Applications, vol. 215. Elsevier BV, p. 119327, Apr. 2023. doi:10.1016/j.eswa.2022.119327.
[31]C. Zhong, G. Li, and Z. Meng, “Beluga whale optimization: A novel nature-inspired metaheuristic algorithm,” Knowledge-Based Systems, vol. 251. Elsevier BV, p. 109215, Sep. 2022. doi:10.1016/j.knosys.2022.109215.
[32]E. H. Houssein and A. Sayed, “Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification,” Mathematics, vol. 11, no. 3. MDPI AG, p. 707, Jan. 30, 2023. doi:10.3390/math11030707.
[33]S. Mirjalili, “SCA: A Sine Cosine Algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 96. Elsevier BV, pp. 120–133, Mar. 2016. doi:10.1016/j.knosys.2015.12.022.
[34]S. Arora and S. Singh, “Butterfly optimization algorithm: a novel approach for global optimization,” Soft Computing, vol. 23, no. 3. Springer Science and Business Media LLC, pp. 715–734, Mar. 08, 2018. doi:10.1007/s00500-018-3102-4.
[35]S. Arora and S. Singh, “An improved butterfly optimization algorithm with chaos,” Journal of Intelligent & Fuzzy Systems, vol. 32, no. 1. SAGE Publications, pp. 1079–1088, Jan. 13, 2017. doi:10.3233/jifs-16798.
[36]A. Mortazavi and M. Moloodpoor, “Enhanced Butterfly Optimization Algorithm with a New fuzzy Regulator Strategy and Virtual Butterfly Concept,” Knowledge-Based Systems, vol. 228. Elsevier BV, p. 107291, Sep. 2021. doi:10.1016/j.knosys.2021.107291.
[37]M. D. Phung and Q. P. Ha, “Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization,” Applied Soft Computing, vol. 107. Elsevier BV, p. 107376, Aug. 2021. doi:10.1016/j.asoc.2021.107376.
[38]S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95. Elsevier BV, pp. 51–67, May 2016. doi:10.1016/j.advengsoft.2016.01.008.
[39]R. Jiang, M. Yang, S. Wang, and T. Chao, “An improved whale optimization algorithm with armed force program and strategic adjustment,” Applied Mathematical Modelling, vol. 81. Elsevier BV, pp. 603–623, May 2020. doi:10.1016/j.apm.2020.01.002.
[40]Y. Huang, Y. Li, Z. Zhang, and Q. Sun, “A novel path planning approach for AUV based on improved whale optimization algorithm using segment learning and adaptive operator selection,” Ocean Engineering, vol. 280. Elsevier BV, p. 114591, Jul. 2023. doi:10.1016/j.oceaneng.2023.114591.
[41]W. Guo, T. Liu, F. Dai, and P. Xu, “An improved whale optimization algorithm for forecasting water resources demand,” Applied Soft Computing, vol. 86. Elsevier BV, p. 105925, Jan. 2020. doi:10.1016/j.asoc.2019.105925.
[42]M. Hooshyar and Y.-M. Huang, “Meta-heuristic Algorithms in UAV Path Planning Optimization: A Systematic Review (2018–2022),” Drones, vol. 7, no. 12, p. 687, Nov. 2023, doi: 10.3390/drones7120687.
[43]S. Agrawal, B. K. Patle, and S. Sanap, “A systematic review on metaheuristic approaches for autonomous path planning of unmanned aerial vehicles,” Drone Syst. Appl., vol. 12, pp. 1–28, Jan. 2024, doi: 10.1139/dsa-2023-0093.
[44]M. S. Adam, N. F. Abdullah, A. Abu-Samah, O. A. Amodu, and R. Nordin, “Advanced Path Planning for UAV Swarms in Smart City Disaster Scenarios Using Hybrid Metaheuristic Algorithms,” Drones, vol. 9, no. 1, p. 64, Jan. 2025, doi: 10.3390/drones9010064.
[45]B. Baidya, A. Mondal, S. Mallick, and S. Anamalamudi, “Energy-Efficient UAV Path Planning Using PSO-ABC Algorithm in Obstacle-Rich Environments,” 2024 OITS International Conference on Information Technology (OCIT). IEEE, pp. 199–204, Dec. 12, 2024. doi: 10.1109/ocit65031.2024.00043.
[46]G. Altun and İ. Aydın, “Optimizing Unmanned Vehicle Navigation: A Hybrid PSO-GWO Algorithm for Efficient Route Planning,” Firat University Journal of Experimental and Computational Engineering, vol. 4, no. 1, pp. 100–114, Feb. 2025, doi: 10.62520/fujece.1501508.
[47]N. Tenniche and B. Mendil, “A New Hybrid Water Cycle Algorithm for Path Planning with Obstacle Avoidance for Indoor Assistant Autonomous UAV Navigation,” Period. Polytech. Elec. Eng. Comp. Sci., vol. 69, no. 2, pp. 175–187, Apr. 2025, doi: 10.3311/ppee.39765.
[48]G. Hu, F. Huang, B. Shu, and G. Wei, “MAHACO: Multi-algorithm hybrid ant colony optimizer for 3D path planning of a group of UAVs,” Information Sciences, vol. 694, p. 121714, Mar. 2025, doi: 10.1016/j.ins.2024.121714.
[49]R. Özdağ, “A novel hybrid path planning method for sweep coverage of multiple UAVs,” J Supercomput, vol. 81, no. 1, Oct. 2024, doi: 10.1007/s11227-024-06574-z.
[50]Q. Shen, D. Zhang, M. Xie, and Q. He, “Multi-Strategy Enhanced Dung Beetle Optimizer and Its Application in Three-Dimensional UAV Path Planning,” Symmetry, vol. 15, no. 7, p. 1432, Jul. 2023, doi: 10.3390/sym15071432.
[51]E. Belge, A. Altan, and R. Hacıoğlu, “Metaheuristic Optimization-Based Path Planning and Tracking of Quadcopter for Payload Hold-Release Mission,” Electronics, vol. 11, no. 8, p. 1208, Apr. 2022, doi: 10.3390/electronics11081208.
[52]S. Aslan and T. Erkin, “DUALIPA: a new immune plasma algorithm for path planning of unmanned aerial vehicles,” Cluster Comput, vol. 28, no. 4, Feb. 2025, doi: 10.1007/s10586-024-04941-2.
[53]M. Wu et al., “Multi-strategy hybrid adaptive dung beetle optimization for UAV photogrammetric 3D path planning under complex constraints,” Sci Rep, vol. 15, no. 1, Apr. 2025, doi: 10.1038/s41598-025-98563-2.
[54]K. R. Opara and J. Arabas, “Differential Evolution: A survey of theoretical analyses,” Swarm and Evolutionary Computation, vol. 44. Elsevier BV, pp. 546–558, Feb. 2019. doi:10.1016/j.swevo.2018.06.010.

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