Physics-Inspired Hamiltonian Particle Swarm Optimisation for Multi-Agent Movement
References
- A. Nitti, M. D. de Tullio, I. Federico, and G. Carbone, “A collective intelligence model for swarm robotics applications,” Nat. Commun., vol. 16, Jul. 2025, Art. no. 6572. https://doi.org/10.1038/s41467-025-61985-7
- A. Ayali and G. A. Kaminka, “The hybrid bio-robotic swarm as a powerful tool for collective motion research: a perspective,” Frontiers in Neurorobotics, vol. 17, Jul. 2023. https://doi.org/10.3389/fnbot.2023.1215085
- A. P. Engelbrecht, Computational Intelligence. An Introduction, 2nd ed. John Wiley & Sons, Ltd., 2007. https://dai.fmph.uniba.sk/courses/ICI/engelbrecht.comp-intel-intro.07.pdf
- G. G. Rigatos, “Multi-robot motion planning using swarm intelligence,” Int. J. Adv. Robotic Syst., vol. 5, no. 2, Jan. 2008, Art. no. 23. https://doi.org/10.5772/5601
- E. H. Cui, Z. Zhang, C. J. Chen, and W. K. Wong, “Applications of nature-inspired metaheuristic algorithms for tackling optimisation problems across disciplines,” Scientific. Rep., vol. 14, Apr. 2024, Art. no. 9403. https://doi.org/10.1038/s41598-024-56670-6
- X.-S. Yang, “Nature-inspired optimisation algorithms: Challenges and open problems,” J. Comput. Sci., vol. 46, Oct. 2020, Art. no. 101104. https://doi.org/10.1016/j.jocs.2020.101104
- G. Rossides, B. Metcalfe, and A. Hunter, “Particle swarm Optimisation – An adaptation for the control of robotic swarms,” Robotics, vol. 10, no. 2, Apr. 2021, Art. no. 58. https://doi.org/10.3390/robotics10020058
- M. G. M. Hamami and Z. H. Ismail, “A systematic review on particle swarm optimisation towards target search in the swarm robotics domain,” Arch. Comput. Methods Eng., Oct. 2022. https://doi.org/10.1007/s11831-022-09819-3
- J. Kennedy and R. Eberhart, “Particle swarm optimisation,” in ICNN’95 – International Conference on Neural Networks, Perth, WA, Australia, Nov. 1995, pp. 1942–1948. https://doi.org/10.1109/icnn.1995.488968
- Y. Shi and R. C. Eberhart, “Parameter selection in particle swarm optimisation,” in Evolutionary Programming VII, V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, Eds. Springer Berlin Heidelberg, 1998, pp. 591–600. https://doi.org/10.1007/BFb0040810
- E. H. Houssein, A. G. Gad, K. Hussain, and P. N. Suganthan, “Major advances in particle swarm optimisation: Theory, analysis, and application,” Swarm and Evolutionary Computation, vol. 63, June 2021, Art. no. 100868. https://doi.org/10.1016/j.swevo.2021.100868
- A. G. Gad, “Particle swarm optimisation algorithm and its applications: A systematic review,” Archives of Computational Methods in Engineering, vol. 29, pp. 2531–2561, Apr. 2022. https://doi.org/10.1007/s11831-021-09694-4
- A. Gurko and Y. Petrenko, “A PSO-based controller tuning for a laser technical vision system,” in 2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek), Kharkiv, Ukraine, Oct. 2022, pp. 1–5. https://doi.org/10.1109/KhPIWeek57572.2022.9916393
- M. G. Skarpetis, N. D. Kouvakas, F. N. Koumboulis, and M. Tsoukalas, “PSO-based robust control of SISO systems with application to a hydraulic inverted pendulum,” Eng., vol. 6, no. 7, Jul. 2025, Art. no. 146. https://doi.org/10.3390/eng6070146
- E. Cengil, “The power of machine learning methods and PSO in air quality prediction,” Applied Sciences, vol. 15, no. 5, Feb. 2025, Art. no. 2546. https://doi.org/10.3390/app15052546
- D. E. Ghersi, N. E. Mougari, K. Loubar, M. Amoura, and U. Desideri, “A machine learning and Particle Swarm Optimisation approach for desiccant wheel modeling and performance prediction,” Applied Thermal Engineering, vol. 284, Jan. 2026, Art. no. 129084. https://doi.org/10.1016/j.applthermaleng.2025.129084
- Y. Zhang, N. Li, Y. Chen, and Y. Liu, “A mobile robot path planning method based on dynamic multipopulation particle swarm optimisation,” Journal of Robotics, Dec. 2024. https://doi.org/10.1155/joro/5018491
- B. Bratina et al., “Mobile robot localization based on the PSO algorithm with local minima avoiding the fitness function,” Sensors, vol. 25, no. 20, Oct. 2025, Art. no. 6283. https://doi.org/10.3390/s25206283
- M. B. Aremu, G. Ahmed, S. Elferik, and A. W. A. Sarif, “Autonomous mobile robot path planning techniques – A review: metaheuristic and cognitive techniques,” Robotics, vol. 15, no. 1, Jan. 2026, Art. no. 23. https://doi.org/10.3390/robotics15010023
- J. Ni, Y. Zhao, Z. Zhang, C. Ke, and S. X. Yang, “A survey on theories and applications for multi-robot cooperative hunting,” Robotics and Autonomous Systems, vol. 197, Mar. 2026, Art. no. 105296. https://doi.org/10.1016/j.robot.2025.105296
- N. M. Kwok, V. T. Ngo, and Q. P. Ha. “PSO-based cooperative control of multiple mobile robots in parameter-tuned formations,” in 2007 IEEE International Conference on Automation Science and Engineering, Scottsdale, AZ, USA, Sep. 2007, pp. 332–337. https://doi.org/10.1109/COASE.2007.4341716
- X. Wang, D. Yang, and S. Chen, “Particle swarm optimisation based leader-follower cooperative control in multi-agent systems,” Applied Soft Computing, vol. 151, Jan. 2024, Art. no. 111130. https://doi.org/10.1016/j.asoc.2023.111130
- N. Gomez et al., “Leader-follower behavior in multi-agent systems for search and rescue based on PSO approach,” in SoutheastCon 2022, Mobile, AL, USA, Mar.–Apr. 2022, pp. 413–420. https://doi.org/10.1109/SoutheastCon48659.2022.9764133
- O. Boutalbi, F. Seghir, A. Boutalbi, and L. Guerra, “A PSO-based global path planning approach for mobile robots,” in 2024 12th International Conference on Systems and Control (ICSC), Batna, Algeria, Nov. 2024, pp. 354–359. https://doi.org/10.1109/ICSC63929.2024.10928795
- E. A. Aner, M. I. Awad, and O. M. Shehata, “Performance evaluation of PSO-PID and PSO-FLC for continuum robot’s developed modeling and control,” Scientific Reports, vol. 14, Jan. 2024. https://doi.org/10.1038/s41598-023-50551-0
- A. Ayari and S. Bouamama, “A new multiple robot path planning algorithm: dynamic distributed particle swarm optimisation,” Robotics and Biomimetics, vol. 4, Nov. 2017, Art. no. 8. https://doi.org/10.1186/s40638-017-0062-6
- B. Sahu, P. K. Das, and M. R. Kabat, “Multi-robot cooperation and path planning for stick transporting using improved Q-learning and democratic robotics PSO,” Journal of Computational Science, vol. 60, Apr. 2022, Art. no. 101637. https://doi.org/10.1016/j.jocs.2022.101637
- Y.-L. Poy et al., “Enhanced particle swarm optimisation for multi-robot path planning with Bézier curve smoothing,” Robotics, vol. 13, no. 10, Sep. 2024, Art. no. 141. https://doi.org/10.3390/robotics13100141
- Y. Liu, X. Zhang, Y. Zhang, and X. Guan, “Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach,” Chinese Journal of Aeronautics, vol. 32, no. 6, pp. 1504–1519, June 2019. https://doi.org/10.1016/j.cja.2019.03.026
- K. K. Pandey et al., “Trajectory planning and collision control of a mobile robot: A penalty-based PSO approach,” Mathematical Problems in Engineering, Jan. 2023. https://doi.org/10.1155/2023/1040461
- V. Garg, A. Shukla, and R. Tiwari, “AERPSO – An adaptive exploration robotic PSO based cooperative algorithm for multiple target searching,” Expert Systems with Applications, vol. 209, Dec. 2022, Art. no. 118245. https://doi.org/10.1016/j.eswa.2022.118245
- A. Benmachiche, M. Derdour, M. S. Kahil, M. C. Ghanem, and M. Deriche, “Adaptive hybrid PSO-APF algorithm for advanced path planning in next-generation autonomous robots,” Sensors, vol. 25, no. 18, Sep. 2025, Art. no. 5742. https://doi.org/10.3390/s25185742
- S. M. Mikki and A. A. Kishk, Particle Swarm Optimisation: A Physics-Based Approach. Springer International Publishing, 2008. https://doi.org/10.1007/978-3-031-01704-9
- E. Can, “Energy-aware adaptive altitude aontrol of UAVs via fuzzy-PSO optimisation within a port-hamiltonian framework under icing and sensor noise,” International Journal of Aeronautical and Space Sciences, pp. 2552–2568, Nov. 2025. https://doi.org/10.1007/s42405-025-01087-2
- O. Sinkevych, B. Sokolovskyy, Y. Boyko, and Z. Matchyshyn, “Physics-informed particle swarm optimisation for collision-aware swarm navigation,” Advances in Cyber-Physical Systems, vol. 10, no. 2, pp. 197–201, Nov. 2025. https://doi.org/10.23939/acps2025.02.197
- E. Sebastián, T. Duong, N. Atanasov, E. Montijano, and C. Sagues,“Physics-informed multiagent reinforcement learning for distributed multirobot problems,” IEEE Transactions on Robotics, vol. 41, pp. 4499–4517, June 2025. https://doi.org/10.1109/tro.2025.3582836
- K. Bojappa and J. Lee, “Thermodynamic particle swarm optimisation for multi-agent system in unknown environment,” IFAC-PapersOnLine, vol. 59, no. 30, pp. 461–466, 2025. https://doi.org/10.1016/j.ifacol.2025.12.280
- J. R. Mohallem, Lagrangian and Hamiltonian Mechanics. Cham, Switzerland: Springer International Publishing, May 2025.
Language: English
Page range: 63 - 73
Submitted on: Mar 17, 2026
Accepted on: Apr 8, 2026
Published on: Apr 26, 2026
Published by: Riga Technical University
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
Related subjects:
© 2026 Oleh Sinkevych, Bohdan Sokolovskii, Yaroslav Boyko, Igor Olenych, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.