Volume 8 • Issue 1 • PP: 15–28 • 2024
Football Optimization Algorithm (FbOA): A Novel Metaheuristic Inspired by Team Strategy Dynamics
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
The Football Optimization Algorithm (FbOA) is introduced as a novel population-based metaheuristic optimization technique inspired by the dynamic strategies of a football team. Designed to address complex optimization problems characterized by high dimensionality, nonlinearity, and multiple local optima, FbOA draws on the strategic balance between exploration and exploitation observed in football gameplay. The algorithm mimics players’ tactical positioning and movement, incorporating short passes, long passes, and positional adjustments to explore and exploit the solution space effectively. This study comprehensively evaluates the performance of FbOA using benchmark functions from the CEC 2005 test suite with 30-dimensional and 100-dimensional optimization problems. The results demonstrate that FbOA outperforms several state-of-the-art metaheuristic algorithms regarding convergence speed, accuracy, and robustness. The findings suggest that FbOA offers a promising alternative for solving various optimization challenges across multiple fields.
Keywords
References
[1] L. Abualigah, M. A. Elaziz, A. M. Khasawneh, M. Alsh-inwan, R. A. Ibrahim, M. A. A. Al-qaness, S. Mirjalili, P. Sumari, and A. H. Gandomi, “Meta-heuristic opti-mization algorithms for solving real-world mechanical engineering design problems: A comprehensive survey, applications, comparative analysis, and results,” Neural Computing and Applications, vol. 34, no. 6, pp. 4081–4110, 2022.
[2] A. Altan, “Performance of metaheuristic optimization algorithms based on swarm intelligence in attitude and altitude control of unmanned aerial vehicle for path following,” in 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2020, pp. 1–6.
[3] M. Dehghani, Z. Montazeri, E. Trojovská, and P. Tro-jovský, “Coati optimization algorithm: A new bio-inspired metaheuristic algorithm for solving optimiza-tion problems,” Knowledge-Based Systems, vol. 259, p. 110011, 2023.
[4] J. Fan and X. Zhou, “Optimization of a hybrid so-lar/wind/storage system with bio-generator for a house-hold by emerging metaheuristic optimization algorithm,” Journal of Energy Storage, vol. 73, p. 108967, 2023.
[5] C. Iwendi, P. K. R. Maddikunta, T. R. Gadekallu, K. Lakshmanna, A. K. Bashir, and M. J. Piran, “A meta-heuristic optimization approach for energy efficiency in the IoT networks,” Software: Practice and Experience, vol. 51, no. 12, pp. 2558–2571, 2021.
[6] M. Jahangiri, M. A. Hadianfard, M. A. Najafgholipour, M. Jahangiri, and M. R. Gerami, “Interactive autodidac-tic school: A new metaheuristic optimization algorithm for solving mathematical and structural design optimiza-tion problems,” Computers & Structures, vol. 235, p. 106268, 2020.
[7] J. Li, C. Li, and S. Zhang, “Application of six meta-heuristic optimization algorithms and random forest in the uniaxial compressive strength of rock prediction,” Applied Soft Computing, vol. 131, p. 109729, 2022.
[8] A. Mahmoodzadeh, H. R. Nejati, M. Mohammadi, H. H. Ibrahim, M. Khishe, S. Rashidi, and H. F. Hama Ali, “Prediction of mode-i rock fracture toughness using sup-port vector regression with metaheuristic optimization algorithms,” Engineering Fracture Mechanics, vol. 264, p. 108334, 2022.
[9] I. Matoušová, P. Trojovský, M. Dehghani, E. Trojovská, and J. Kostra, “Mother optimization algorithm: A new human-based metaheuristic approach for solving engi-neering optimization,” Scientific Reports, vol. 13, no. 1, p. 10312, 2023.
[10] N.-T. Ngo, T. T. H. Truong, N.-S. Truong, A.-D. Pham, N.-T. Huynh, T. M. Pham, and V. H. S. Pham, “Propos-ing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings,” Scientific Reports, vol. 12, no. 1, p. 1065, 2022.
[11] E. Osaba, E. Villar-Rodriguez, J. Del Ser, A. J. Ne-bro, D. Molina, A. LaTorre, P. N. Suganthan, C. A. Coello Coello, and F. Herrera, “A tutorial on the design, experimentation and application of metaheuristic algo-rithms to real-world optimization problems,” Swarm and Evolutionary Computation, vol. 64, p. 100888, 2021.
[12] M. H. Qais, H. M. Hasanien, and S. Alghuwainem, “Transient search optimization: A new meta-heuristic optimization algorithm,” Applied Intelligence, vol. 50, no. 11, pp. 3926–3941, 2020.
[13] H. T. Sadeeq and A. M. Abdulazeez, “Giant trevally optimizer (GTO): A novel metaheuristic algorithm for global optimization and challenging engineering prob-lems,” IEEE Access, vol. 10, pp. 121 615–121 640, 2022.
[14] S. Talatahari and M. Azizi, “Chaos game optimization: A novel metaheuristic algorithm,” Artificial Intelligence Review, vol. 54, no. 2, pp. 917–1004, 2021.
[15] P. Wang, Y. Zhou, Q. Luo, C. Han, Y. Niu, and M. Lei, “Complex-valued encoding metaheuristic optimization algorithm: A comprehensive survey,” Neurocomputing, vol. 407, pp. 313–342, 2020.
[16] M. Abdel-Basset, D. El-Shahat, M. Jameel, and M. Abouhawwash, “Young’s double-slit experiment op-timizer: A novel metaheuristic optimization algorithm for global and constraint optimization problems,” Com-puter Methods in Applied Mechanics and Engineering, vol. 403, p. 115652, 2023.
[17] M. A. Akbari, M. Zare, R. Azizipanah-abarghooee, S. Mirjalili, and M. Deriche, “The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems,” Scientific Reports, vol. 12, no. 1, p. 10953, 2022.
[18] M. Azizi, U. Aickelin, H. A. Khorshidi, and M. Baghalzadeh Shishehgarkhaneh, “Energy valley op-timizer: A novel metaheuristic algorithm for global and engineering optimization,” Scientific Reports, vol. 13, no. 1, p. 226, 2023.
[19] E. Belge, A. Altan, and R. Hacıo˘glu, “Metaheuristic optimization-based path planning and tracking of quad-copter for payload hold-release mission,” Electronics, vol. 11, no. 8, 2022.
[20] A. F. Güven and M. M. Samy, “Performance analysis of autonomous green energy system based on multi and hybrid metaheuristic optimization approaches,” En-ergy Conversion and Management, vol. 269, p. 116058, 2022.
[21] H. N. Ghafil and K. Jármai, “Dynamic differential an-nealed optimization: New metaheuristic optimization algorithm for engineering applications,” Applied Soft Computing, vol. 93, p. 106392, 2020.
[22] A. M. Helmi, R. Carli, M. Dotoli, and H. S. Ramadan, “Efficient and sustainable reconfiguration of distribu-tion networks via metaheuristic optimization,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 1, pp. 82–98, 2022.
[23] M. H. Qais, H. M. Hasanien, R. A. Turky, S. Al-ghuwainem, M. Tostado-Véliz, and F. Jurado, “Circle search algorithm: A geometry-based metaheuristic op-timization algorithm,” Mathematics, vol. 10, no. 10, 2022.
[24] M. H. Sulaiman and Z. Mustaffa, “Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers,” Results in Control and Optimization, vol. 8, p. 100145, 2022.
[25] Q. Zhang, H. Gao, Z.-H. Zhan, J. Li, and H. Zhang, “Growth optimizer: A powerful metaheuristic algorithm for solving continuous and discrete global optimiza-tion problems,” Knowledge-Based Systems, vol. 261, p. 110206, 2023.
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.