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Journal of Artificial Intelligence and Metaheuristics

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Journal of Artificial Intelligence and Metaheuristics

Volume 8 , Issue 1 , PP: 21-38, | Cite this article as | XML | Html | PDF | Full Length Article

Football Optimization Algorithm (FbOA): A Novel Metaheuristic Inspired by Team Strategy Dynamics

El-Sayed M. El-Kenawy 1 , Faris H. Rizk 2 , Ahmed Mohamed Zaki 3 , Mahmoud Elshabrawy Mohamed 4 , Abdelhameed Ibrahim 5 , Abdelaziz A. Abdelhamid 6 , Nima Khodadadi 7 , Ehab M. Almetwally 8 , Marwa M. Eid 9

  • 1 School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain; Jadara University Research Center, Jadara University, Jordan; Applied Science Research Center. Applied Science Private University, Amman, Jordan; Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt - (skenawy@ieee.org)
  • 2 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA - (faris.rizk@jcsis.org)
  • 3 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA - (Azaki@jcsis.org)
  • 4 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA - (mshabrawy@jcsis.org)
  • 5 School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain - (abdelhameed.fawzy@polytechnic.bh)
  • 6 Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt; Department of Computer Science, College of Computing and Information Technology, Shaqra University, 11961, Shaqra, Saudi Arabia - (abdelaziz@cis.asu.edu.eg)
  • 7 Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA - (nima.khodadadi@miami.edu)
  • 8 Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia - (emalmetwally@imamu.edu.sa)
  • 9 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt - (mmm@ieee.org)

    • Doi: https://doi.org/10.54216/JAIM.080103

    Received: March 12, 2024 Revised: May 13, 2024 Accepted: August 11, 2024
    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 :

    Football Optimization Algorithm, Metaheuristic Optimization, Population-based Algorithm, Complex Problem Solving, Team Strategy Dynamics

    References

    [1] L. Abualigah, M. A. Elaziz, A. M. Khasawneh, M. Alshinwan, R. A. Ibrahim, M. A. A. Al-qaness, S. Mirjalili, P. Sumari, and A. H. Gandomi. Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: A comprehensive survey, applications, comparative analysis, and results. Neural Computing and Applications, 34(6):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), pages 1–6, 2020.

    [3] M. Dehghani, Z. Montazeri, E. Trojovsk´a, and P. Trojovsk´y. Coati optimization algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems, 259:110011, 2023.

    [4] J. Fan and X. Zhou. Optimization of a hybrid solar/wind/storage system with bio-generator for a household by emerging metaheuristic optimization algorithm. Journal of Energy Storage, 73:108967, 2023.

    [5] C. Iwendi, P. K. R. Maddikunta, T. R. Gadekallu, K. Lakshmanna, A. K. Bashir, and Md. J. Piran. A metaheuristic optimization approach for energy efficiency in the iot networks. Software: Practice and Experience, 51(12):2558–2571, 2021.

    [6] M. Jahangiri, M. A. Hadianfard, M. A. Najafgholipour, M. Jahangiri, and M. R. Gerami. Interactive autodidactic school: A new metaheuristic optimization algorithm for solving mathematical and structural design optimization problems. Computers & Structures, 235:106268, 2020.

    [7] J. Li, C. Li, and S. Zhang. Application of six metaheuristic optimization algorithms and random forest in the uniaxial compressive strength of rock prediction. Applied Soft Computing, 131:109729, 2022.

    [8] A. Mahmoodzadeh, H. R. Nejati, M. Mohammadi, H. Hashim Ibrahim, M. Khishe, S. Rashidi, and H. Farid Hama Ali. Prediction of mode-i rock fracture toughness using support vector regression with metaheuristic optimization algorithms. Engineering Fracture Mechanics, 264:108334, 2022.

    [9] I. Matouˇsov´a, P. Trojovsk´y, M. Dehghani, E. Trojovsk´a, and J. Kostra. Mother optimization algorithm: A new human-based metaheuristic approach for solving engineering optimization. Scientific Reports, 13(1):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. Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings. Scientific Reports, 12(1):1065, 2022.

    [11] E. Osaba, E. Villar-Rodriguez, J. Del Ser, A. J. Nebro, D. Molina, A. LaTorre, P. N. Suganthan, C. A. Coello Coello, and F. Herrera. A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm and Evolutionary Computation, 64:100888, 2021.

    [12] M. H. Qais, H. M. Hasanien, and S. Alghuwainem. Transient search optimization: A new meta-heuristic optimization algorithm. Applied Intelligence, 50(11):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 problems. IEEE Access, 10:121615–121640, 2022.

    [14] S. Talatahari and M. Azizi. Chaos game optimization: A novel metaheuristic algorithm. Artificial Intelligence Review, 54(2):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, 407:313–342, 2020.

    [16] M. Abdel-Basset, D. El-Shahat, M. Jameel, and M. Abouhawwash. Young’s double-slit experiment optimizer: A novel metaheuristic optimization algorithm for global and constraint optimization problems. Computer Methods in Applied Mechanics and Engineering, 403:115652, 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, 12(1):10953, 2022.

    [18] M. Azizi, U. Aickelin, H. A. Khorshidi, and M. Baghalzadeh Shishehgarkhaneh. Energy valley optimizer: A novel metaheuristic algorithm for global and engineering optimization. Scientific Reports, 13(1):226, 2023.

    [19] E. Belge, A. Altan, and R. Hacıo˘glu. Metaheuristic optimization-based path planning and tracking of quadcopter for payload hold-release mission. Electronics, 11(8), 2022.

    [20] A. Fatih G¨uven and M. Mahmoud Samy. Performance analysis of autonomous green energy system based on multi and hybrid metaheuristic optimization approaches. Energy Conversion and Management, 269:116058, 2022.

    [21] H. N. Ghafil and K. J´armai. Dynamic differential annealed optimization: New metaheuristic optimization algorithm for engineering applications. Applied Soft Computing, 93:106392, 2020.

    [22] A. M. Helmi, R. Carli, M. Dotoli, and H. S. Ramadan. Efficient and sustainable reconfiguration of distribution networks via metaheuristic optimization. IEEE Transactions on Automation Science and Engineering, 19(1):82–98, 2022.

    [23] M. H. Qais, H. M. Hasanien, R. A. Turky, S. Alghuwainem, M. Tostado-V´eliz, and F. Jurado. Circle search algorithm: A geometry-based metaheuristic optimization algorithm. Mathematics, 10(10), 2022.

    Cite This Article As :
    M., El-Sayed. , H., Faris. , Mohamed, Ahmed. , Elshabrawy, Mahmoud. , Ibrahim, Abdelhameed. , A., Abdelaziz. , Khodadadi, Nima. , M., Ehab. , M., Marwa. Football Optimization Algorithm (FbOA): A Novel Metaheuristic Inspired by Team Strategy Dynamics. Journal of Artificial Intelligence and Metaheuristics, vol. , no. , , pp. 21-38. DOI: https://doi.org/10.54216/JAIM.080103
    M., E. H., F. Mohamed, A. Elshabrawy, M. Ibrahim, A. A., A. Khodadadi, N. M., E. M., M. (). Football Optimization Algorithm (FbOA): A Novel Metaheuristic Inspired by Team Strategy Dynamics. Journal of Artificial Intelligence and Metaheuristics, (), 21-38. DOI: https://doi.org/10.54216/JAIM.080103
    M., El-Sayed. H., Faris. Mohamed, Ahmed. Elshabrawy, Mahmoud. Ibrahim, Abdelhameed. A., Abdelaziz. Khodadadi, Nima. M., Ehab. M., Marwa. Football Optimization Algorithm (FbOA): A Novel Metaheuristic Inspired by Team Strategy Dynamics. Journal of Artificial Intelligence and Metaheuristics , no. (): 21-38. DOI: https://doi.org/10.54216/JAIM.080103
    M., E. , H., F. , Mohamed, A. , Elshabrawy, M. , Ibrahim, A. , A., A. , Khodadadi, N. , M., E. , M., M. () . Football Optimization Algorithm (FbOA): A Novel Metaheuristic Inspired by Team Strategy Dynamics. Journal of Artificial Intelligence and Metaheuristics , () , 21-38 . DOI: https://doi.org/10.54216/JAIM.080103
    M. E. , H. F. , Mohamed A. , Elshabrawy M. , Ibrahim A. , A. A. , Khodadadi N. , M. E. , M. M. []. Football Optimization Algorithm (FbOA): A Novel Metaheuristic Inspired by Team Strategy Dynamics. Journal of Artificial Intelligence and Metaheuristics. (): 21-38. DOI: https://doi.org/10.54216/JAIM.080103
    M., E. H., F. Mohamed, A. Elshabrawy, M. Ibrahim, A. A., A. Khodadadi, N. M., E. M., M. "Football Optimization Algorithm (FbOA): A Novel Metaheuristic Inspired by Team Strategy Dynamics," Journal of Artificial Intelligence and Metaheuristics, vol. , no. , pp. 21-38, . DOI: https://doi.org/10.54216/JAIM.080103

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