International Journal of Engineering

International Journal of Engineering

Bermuda Weed Optimization: A Scalable Meta-heuristic for Cloud-Based Cruise Control Systems

Document Type : Original Article

Authors
M. Allahi Rudposhti
A. Bohlooli
K. Jamshidi
Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran
10.5829/ije.2026.39.10a.15
Abstract
This study proposes the Bermuda Weed Optimization (BWO) algorithm; a novel and scalable metaheuristic algorithm inspired by the invasive growth of Bermuda grass. This algorithm has been developed as an enhanced version of the Invasive Weed Optimization (IWO) algorithm and, by imitating the plant's robust propagation strategies, achieves a better balance between global exploration and local exploitation. The algorithm's performance was rigorously evaluated against four previous IWO-based versions, and its superior scalability was demonstrated through the lowest average error and stable performance across diverse scenarios. Furthermore, BWO was compared with the new Gray Squirrel Search Algorithm (GSFA)—which falls outside the IWO category—to assess its performance against a novel method unrelated to the IWO family; this comparison highlighted BWO's competitive superiority and achieved an average 64.43% improvement in best-cost results. The strong convergence and scalability of BWO make it highly suitable for real-time applications—particularly in automotive systems. In a practical implementation using a cloud-based cruise control (CC) framework, BWO significantly outperformed the RPO-based method (the latest approach) by reducing overshoot by 45.92%, settling time by 29.38%, ISE speed by 8.92%, and maximum jerk by 20.09%. By achieving near-optimal convergence and leveraging cloud deployment with high scalability, BWO can effectively adapt to diverse automotive system requirements and achieve high efficiency across multiple operating modes. 

Graphical Abstract

Bermuda Weed Optimization: A Scalable Meta-heuristic for Cloud-Based Cruise Control Systems
Keywords
Stochastic Optimization
Cruise Control
Bermuda Weed Optimization
Meta-heuristic Algorithms

Subjects

1.    Mirjalili S, Lewis A. The whale optimization algorithm. Advances in Engineering Software, 2016;95:51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008
2.    Holland JH. Genetic algorithms. Scientific American, 1992;267(1):66-72. https://doi.org/10.1038/scientificamerican0792-66
3.    Kahrizi MR, Kabudian SJ. Projectiles optimization: A novel metaheuristic algorithm for global optimization. International Journal of Engineering Transactions A: Basics, 2020;33(10):1924-38. https://doi.org/10.5829/ije.2020.33.10a.11
4.    El-Shorbagy MA, Bouaouda A, Nabwey HA, Abualigah L, Hashim FA. Advances in Henry gas solubility optimization: A physics-inspired metaheuristic algorithm with its variants and applications. IEEE Access, 2024;12:26062-95. https://doi.org/10.1109/ACCESS.2024.3365700
5.    Sowmya R, Premkumar M, Jangir P. Newton–Raphson-based optimizer: A new population-based metaheuristic algorithm for continuous optimization problems. Engineering Applications of Artificial Intelligence, 2024;128:107532. https://doi.org/10.1016/j.engappai.2023.107532
6.    Mehrabian AR, Lucas C. A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 2006;1(4):355-66. https://doi.org/10.1016/j.ecoinf.2006.07.003
7.    Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Advances in Engineering Software, 2014;69:46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
8.    Saremi S, Mirjalili S, Lewis A. Grasshopper optimisation algorithm: Theory and application. Advances in Engineering Software, 2017;105:30-47. https://doi.org/10.1016/j.advengsoft.2017.01.004
9.    Talatahari S, Azizi M, Tolouei M, Talatahari B, Sareh P. Crystal structure algorithm (CryStAl): A metaheuristic optimization method. IEEE Access, 2021;9:71244-61. https://doi.org/10.1109/ACCESS.2021.3079161
10.    Zhao S, Zhang T, Ma S, Chen M. Dandelion optimizer: A nature-inspired metaheuristic algorithm for engineering applications. Engineering Applications of Artificial Intelligence, 2022;114:105075. https://doi.org/10.1016/j.engappai.2022.105075
11.    Han M, Du Z, Yuen KF, Zhu H, Li Y, Yuan Q. Walrus optimizer: A novel nature-inspired metaheuristic algorithm. Expert Systems with Applications, 2024;239:122413. https://doi.org/10.1016/j.eswa.2023.122413
12.    Zhong C, Li G, Meng Z, Li H, Yildiz AR, Mirjalili S. Starfish optimization algorithm (SFOA): A bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers. Neural Computing and Applications, 2025;37(5):3641-83. https://doi.org/10.1007/s00521-024-10694-1
13.    Amani B, Nouri M, Mousavi Ghasemi SA. Gray squirrel foraging algorithm for function optimization. International Journal of Engineering, 2026;39(7):1644-56. https://doi.org/10.5829/ije.2026.39.07a.09
14.    Ehsaeyan E. Rock-climbing group: An innovative meta-heuristic approach for efficiently tackling optimization problems. International Journal of Engineering Transactions B: Applications, 2025;38(11):2796-818. https://doi.org/10.5829/ije.2025.38.11b.24
15.    Ehsaeyan E. Gold seekers algorithm: An innovative metaheuristic approach for global optimization and its application in image segmentation. International Journal of Engineering Transactions C: Aspects, 2025;38(9):2114-29. https://doi.org/10.5829/ije.2025.38.09c.09
16.    Perez C, Climent L, Nicolo G, Arbelaez A, Salido MA. A hybrid metaheuristic with learning for a real supply chain scheduling problem. Engineering Applications of Artificial Intelligence, 2023;126:107188. https://doi.org/10.1016/j.engappai.2023.107188
17.    Mallahzadeh A, Eshaghi S, Hassani H. Compact U-array MIMO antenna designs using IWO algorithm. International Journal of RF and Microwave Computer-Aided Engineering, 2009;19(5):568-76. https://doi.org/10.1002/mmce.20379
18.    Pahlavani P, Delavar MR, Frank AU. Using a modified invasive weed optimization algorithm for a personalized urban multi-criteria path optimization problem. International Journal of Applied Earth Observation and Geoinformation, 2012;18:313-28. https://doi.org/10.1016/j.jag.2012.03.004
19.    Ahmadi M, Mojallali H. Chaotic invasive weed optimization algorithm with application to parameter estimation of chaotic systems. Chaos, Solitons & Fractals, 2012;45(9):1108-20. https://doi.org/10.1016/j.chaos.2012.05.010
20.    Nikoofard AH, Hajimirsadeghi H, Rahimi-Kian A, Lucas C. Multiobjective invasive weed optimization: Application to analysis of Pareto improvement models in electricity markets. Applied Soft Computing, 2012;12(1):100-12. https://doi.org/10.1016/j.asoc.2011.09.005
21.    Ghasemi M, Ghavidel S, Akbari E, Vahed AA. Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos. Energy, 2014;73:340-53. https://doi.org/10.1016/j.energy.2014.06.026
22.    Mishra SK, Bose PSC, Rao CSP. An invasive weed optimization approach for job shop scheduling problems. International Journal of Advanced Manufacturing Technology, 2017;91(9):4233-41. https://doi.org/10.1007/s00170-017-0091-x
23.    Mandava RK, Vundavilli PR. Implementation of modified chaotic invasive weed optimization algorithm for optimizing the PID controller of the biped robot. Sadhana, 2018;43(5):66. https://doi.org/10.1007/s12046-018-0851-9
24.    Abdelkader EM, Moselhi O, Marzouk M, Zayed T. A multi-objective invasive weed optimization method for segmentation of distress images. Intelligent Automation & Soft Computing, 2020;26(4):643-61. https://doi.org/10.32604/iasc.2020.010100
25.    Kashyap AK, Parhi D, Pandey A. Improved modified chaotic invasive weed optimization approach to solve multi-target assignment for humanoid robot. Journal of Robotics and Control, 2021;2(3):194-9. https://doi.org/10.18196/jrc.2377
26.    Ibrahim A, Anayi F, Packianather M, Alomari OA. New hybrid invasive weed optimization and machine learning approach for fault detection. Energies, 2022;15(4):1488. https://doi.org/10.3390/en15041488
27.    Beskirli M. A novel invasive weed optimization with levy flight for optimization problems: The case of forecasting energy demand. Energy Reports, 2022;8:1102-11. https://doi.org/10.1016/j.egyr.2021.11.108
28.    Kalhori M, Ashofteh PS, Moghadam SH. Development of the multi-objective invasive weed optimization algorithm in the integrated water resources allocation problem. Water Resources Management, 2023;37(11):4433-58. https://doi.org/10.1007/s11269-023-03564-3
29.    Chauhan U, Chhabra H, Jain P, Dev A, Chauhan N, Kumar B. Chaos inspired invasive weed optimization algorithm for parameter estimation of solar PV models. IFAC Journal of Systems and Control, 2024;27:100239. https://doi.org/10.1016/j.ifacsc.2023.100239
30.    Taliaferro CM, Rouquette FM, Mislevy P. Bermudagrass and stargrass. Agronomy Monographs, 2004;45:417-75. https://doi.org/10.2134/agronmonogr45.c12
31.    Heybetli F, Danayiyen Y, Tasdemir AB, Senyigit S. Comparative analysis of metaheuristic algorithms in PID-based vehicle cruise control systems. Verus Journal, 2025;25(1):1-16. https://doi.org/10.5152/electrica.2025.25051
32.    Pradhan R, Majhi SK, Pradhan JK, Pati BB. Antlion optimizer tuned PID controller based on bode ideal transfer function for automobile cruise control system. Journal of Industrial Information Integration, 2018;9:45-52. https://doi.org/10.1016/j.jii.2018.01.002
33.    Izci D, Ekinci S, Kayri M, Eker E. A novel enhanced metaheuristic algorithm for automobile cruise control system. Electrica, 2021;21(3):283-97. https://doi.org/10.5152/electrica.2021.21016
34.    Prasanna V, Nelson A, Hnaumanthakari S, Kumar VK, Kirubakaran S, Kumar MJ. Metaheuristic algorithm for automatic cruise control system. Proceedings of the 3rd International Conference on Smart Electronics and Communication, 2022:206-11. https://doi.org/10.1109/ICOSEC54921.2022.9952117
35.    Saravanan G, Pazhanimuthu C, Sathish Kumar D, Lalitha B, Senthilkumar M, Kannan E. Red panda optimization algorithm-based PID controller design for automobile cruise control system. Proceedings of the International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering, 2024:33-37. https://doi.org/10.1109/ICSSEECC61126.2024.10649422
36.    Afshar M, Hadji Molana SY, Rahmani Parchicolaie B. A multi-objective optimization model for multi-commodity closed-loop supply chain network considering disruption risk. International Journal of Engineering Transactions A: Basics, 2024;37(4):646-61. https://doi.org/10.5829/ije.2024.37.04a.07
37.    Ebrahimi Gouraji R, Soleimani H, Afshar Najafi B. Optimization of sustainable vehicle routing problem taking into account social utility and employing a strategy with multiple objectives. International Journal of Engineering Transactions A: Basics, 2025;38(7):1631-58. https://doi.org/10.5829/ije.2025.38.07a.15
International Journal of Engineering
Volume 39, Issue 10
TRANSACTIONS A: Basics
October 2026
Pages 2529-2545