Dynamic Leader Selection and Gradient Climbing Techniques in Swarm Optimization

Abstract:

An important challenge of swarm robotics in practical applications lies with optimizing swarm navigation especially in dynamic or time-changing environments, which may affect the swarm’s overall performance. One technique to guide swarm behavior is by the use of the gradient climbing algorithm. This is an optimization technique where agents move towards increasing values of a scalar field, such as heat intensity or gas concentration, based on local gradient information and enables agents to navigate towards areas of interest by iteratively adjusting their positions to maximize the gradient. In complex and dynamic environments, achieving optimality may be difficult if appropriate swarm leadership strategies are absent. Leader selection entails identifying certain agents that may possess superior sensing capabilities, computational power, or strategic positioning within the swarm to guide the swarm behavior and decision-making. Therefore, this study develops an algorithm for dynamic leadership selection in swarm robotics for operations in changing environments such as in forest fires. Using the gradient information, leadership roles are assigned within the swarm to robots with the highest gradient value, which allows the algorithm to adapt to changing environmental conditions and improves the overall navigation towards the desired gradient maxima. The convergence of the swarm to the global maxima is evaluated through simulations, and shows that swarms with dynamic leader selection have convergence times that are less than half of the convergence times obtained in swarms with fixed leaders and swarms with no leaders selected. Also, the algorithm results in a reduced exploration area corresponding to improved energy efficiency when compared to the swarm with fixed leaders. The results demonstrate the effectiveness of dynamic leader selection in optimizing swarm behaviour in changing environments and its potential for real-world applications.

• Periodical: Advanced Engineering Forum (Volume 56)
• Pages: 129-140
• DOI: https://doi.org/10.4028/p-pI4iap
• Online since: June 2025
• Authors: Olukayode Adenekan*, Brendan Ubochi, Nnamdi Nwulu, Kayode Francis Akingbade
• Keywords: Convergence, Dynamic Leadership, Optimization, Swarm Robotics

References

[1] X. Chen, B. Hopkins, H. Wang, L. O'Neill, F. Afghah, A. Razi, A. Watts, Wildland fire detection and monitoring using a drone-collected RGB/IR image dataset, IEEE Access. 10 (2022) 121301-121317.

DOI: 10.1109/access.2022.3222805

[2] C. Burke, S. Wich, K. Kusin, O. McAree, M. E. Harrison, B. Ripoll, S. Longmore, Thermal-drones as a safe and reliable method for detecting subterranean peat fires, Drones. 3, 1 (2019) 23.

DOI: 10.3390/drones3010023

[3] L.F. Oliveira, A.P. Moreira, M. F. Silva, Advances in forest robotics: A state-of-the-art survey, Robotics. 10, 2 (2021) 53.

DOI: 10.3390/robotics10020053

[4] M. Dorigo, G. Theraulaz, V. Trianni, Swarm robotics: Past, present, and future (point of view), in: Proceedings of the IEEE. (2021) 1152-1165

DOI: 10.1109/jproc.2021.3072740

[5] I. Navarro, F. Matıa, An introduction to swarm robotics, ISRN Robotics. (2013) 1-10.

[6] E. Sahin, Swarm robotics: From sources of inspiration to domains of application, in: International workshop on swarm robotics, Springer. (2005) 10-20.

DOI: 10.1007/978-3-540-30552-1_2

[7] R. Miletitch, A. Reina, M. Dorigo, V. Trianni, Emergent naming conventions in a foraging robot swarm" Swarm Intelligence. 16, 3 (2022) 211-232.

DOI: 10.1007/s11721-022-00212-1

[8] R. Sandhu, O. P. Sharma, V. Sharma, A Review on Swarm Robots, International Journal of Advanced Research in Computer and Communication Engineering. 4, 5 (2015) 218-222.

[9] T. M. Laing, S.L. Ng, A. Tomlinson, K. M. Martin, Security in Swarm Robotics, in: Handbook of Research on Design, Control, and Modeling of Swarm Robotics, IGI Global. 2 (2016) 42–66.

DOI: 10.4018/978-1-4666-9572-6.ch002

[10] M. Brambilla, E. Ferrante, M. Birattari, M. Dorigo, Swarm robotics: a review from the swarm engineering perspective, Swarm Intelligence. 7, 1 (2013) 1-41.

DOI: 10.1007/s11721-012-0075-2

[11] M. Schranz, M. Umlauft, M. Sende, W. Elmenreich, Swarm robotic behaviors and current applications, Frontiers in Robotics and AI. 7, 36 (2020) 74-93.

DOI: 10.3389/frobt.2020.00036

[12] R. Sendra-Arranz, A. Gutierrez, Evolution of situated and abstract communication in leader selection and borderline identification swarm robotics problems, Applied Sciences. 11, 8 (2021) 3516.

DOI: 10.3390/app11083516

[13] P. Walker, S. A. Amraii, M. Lewis, N. Chakraborty, K. Sycara, Control of swarms with multiple leader agents, in: IEEE International Conference on Systems, Man, and Cybernetics. (2014) 3567-3572.

DOI: 10.1109/smc.2014.6974483

[14] V. Karpov, I. Karpova, Leader election algorithms for static swarms, Biologically Inspired Cognitive Architectures. 12 (2015) 54-64.

DOI: 10.1016/j.bica.2015.04.001

[15] E. Olcay, K. Gabrich, B. Lohmann, Optimal control of a swarming multi-agent system through guidance of a leader-agent, IFAC-PapersOnLine. 52, 20 (2019) 1-6.

DOI: 10.1016/j.ifacol.2019.12.092

[16] K. Elamvazhuthip, Z. Kakish, A. Shirsat, S. Berman, Controllability and stabilization for herding a robotic swarm using a leader: A mean-field approach, IEEE Transactions on Robotics. 37, 2 (2020) 418-432.

DOI: 10.1109/tro.2020.3031237

[17] A. D. H. B. Tavares, S. P., A. V. Madruga, B.T. P. Nascimento, Dynamic leader allocation in multi-robot systems based on nonlinear model predictive control, Journal of Intelligent & Robotic Systems. 98 (2020) 359-376.

DOI: 10.1007/s10846-019-01064-4

[18] F. Li, Y. Ding, K. Hao, A neuroendocrine inspired dynamic leader selection model in formation control for multi-robot system. In Chinese Control and Decision Conference (2017) 5454 - 5459.

DOI: 10.1109/ccdc.2017.7979466

[19] F. Li, Y. Ding, K. Hao, A dynamic leader-follower strategy for multi-robot systems, in: IEEE International Conference on Systems, Man, and Cybernetics (2015) 298-303.

DOI: 10.1109/smc.2015.64

[20] S. Misra, P. K. Deb, K. Saini, Dynamic leader selection in a master-slave architecture-based micro-UAV swarm. In IEEE Global Communications Conference. (2021) 1-6.

DOI: 10.1109/globecom46510.2021.9685538

[21] C. H. Tesfay, Z. Xiang, L. Yang, M. A. Berwo, J. Mahmood, S. A. Chaudhry, An effective and efficient UAV leader selection scheme in swarm of UAVs, Physical Communication. 67 (2024) 102513

DOI: 10.1016/j.phycom.2024.102513

[22] C. N. Mavridis, N. Suriyarachchi, J. S. Baras, Detection of dynamically changing leaders in complex swarms from observed dynamic data. In International Conference on Decision and Game Theory for Security, Springer International Publishing. (2020) 223-240.

DOI: 10.1007/978-3-030-64793-3_12

[23] MathWorks Inc. (2020). MATLAB [online]. Website https://www.mathworks.com/ products/matlab.html [accessed 20 September 2022]

[24] P. Stein, A. Spalanzani, C. Laugier, V. Santos, Leader selection and following in dynamic environments, in: 12th International Conference on Control Automation Robotics & Vision IEEE. (2012) 124-129.

DOI: 10.1109/icarcv.2012.6485145

[25] Q. Wang, H. Zhang, A self-organizing area coverage method for swarm robots based on gradient and grouping, Symmetry. 13, 4 (2021) 680.

DOI: 10.3390/sym13040680

[26] H. Cai, S. Guo, H. Gao, A dynamic leader–follower approach for line marching of swarm robots, Unmanned Systems, 11, 1 (2023) 67-82.

DOI: 10.1142/s2301385023500024

[27] M. H. A., Majid, M. R. Arshad, R. M. Mokhtar, Swarm robotics behaviors and tasks: a technical review, Control engineering in robotics and industrial automation: Malaysian society for automatic control engineers (MACE) technical series. (2022) 99-167.

DOI: 10.1007/978-3-030-74540-0_5

[28] A. E. Adegunsoye, B. Ubochi, J. Macaulay, K. F. Akingbade, A hybrid gradient climbing algorithm for a swarm robot-based gas leak detector, IAES International Journal of Robotics and Automation. 13, 3 (2024) 255-263

DOI: 10.11591/ijra.v13i3.pp255-263

[29] Y. Elor, A. M. Bruckstein, Robot Cloud gradient climbing with point measurements, Theoretical Computer Science. 547 (2014) 90-103.

DOI: 10.1016/j.tcs.2014.06.025

[30] E. Bıyık, M. Arcak, Gradient climbing in formation via extremum seeking and passivity-based coordination rules, Asian Journal of Control. 10, 2 (2008) 201-211.

DOI: 10.1002/asjc.19

[31] S. J. Garrett, Introduction to Actuarial and Financial Mathematical Methods, Elsevier Inc., London, 2015.

[32] C. Jada, U. Ashok, B. Pavan, P. Vinod-Babu, Butterflies: A new source of inspiration for futuristic aerial robotics, in: Handbook of Smart Materials, Technologies, and Devices: Applications of Industry 4.0, Springer International Publishing. (2022) 813-829.

DOI: 10.1007/978-3-030-84205-5_157

[33] M. Bayati, Using cuckoo optimization algorithm and imperialist competitive algorithm to solve inverse kinematics problem for numerical control of robotic manipulators, in: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 229, 5 (2015) 375-387.

DOI: 10.1177/0959651814568364

[34] M. A. Armada, P. de González Santos, L. Marques, A. T. de Almeida, Finding odours across large search spaces: A particle swarm-based approach, in: Climbing and Walking Robots: Proceedings of the 7th International Conference, Springer. (2005) 419 - 426.

DOI: 10.1007/3-540-29461-9_40

[35] Z. Qiao, J. Zhang, X. Qu, J. Xiong, Dynamic self-organizing leader-follower control in a swarm mobile robots system under limited communication, IEEE Access. 8, (2020) 53850-53856.

DOI: 10.1109/access.2020.2980778