Journal of Cybersecurity and Information Management

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Volume 16 , Issue 1 , PP: 120-133, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Dynamic Leader Sibha Algorithm (DLSA): A Novel Hierarchical Metaheuristic Approach for Solving Engineering Design Problems

El-Sayed M. El-kenawy 1 * , Amel Ali Alhussan 2 , Doaa Sami Khafaga 3 , Amal H. Alharbi 4 , Sarah A. Alzakari 5 , Abdelaziz A. Abdelhamid 6 , Abdelhameed Ibrahim 7 , Marwa M. Eid 8

  • 1 School of ICT, Faculty of Engineering, Design and Information, Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain; Applied Science Research Center. Applied Science Private University, Amman, Jordan; Jadara University Research Center, Jadara University, Jordan - (sayed.elkenawy@polytechnic.bh)
  • 2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia - (aaalhussan@pnu.edu.sa)
  • 3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia - (dskhafga@pnu.edu.sa)
  • 4 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia - (ahalharbi@pnu.edu.sa)
  • 5 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia - (saalzakari@pnu.edu.sa)
  • 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 School of ICT, Faculty of Engineering, Design and Information, Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain - (abdelhameed.fawzy@polytechnic.bh)
  • 8 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt - (mmm@ieee.org)
  • Doi: https://doi.org/10.54216/JCIM.160110

    Received: November 11, 2024 Revised: January 17, 2025 Accepted: February 19, 2025
    Abstract

    We present a new metaheuristic optimization technique, the Dynamic Leader Sibha Algorithm (DLSA), based on the structured dynamics of the ‘Sibha’ (an Islamic tool). Using a hierarchical leader-follower framework, DLSA dynamically balances exploration and exploitation to resolve the difficulties of high dimensional and multimodal optimization. DLSA is applied to three well-known engineering problems, namely the Speed Reducer, Welded Beam, and Pressure Vesseldo, to tackle the objectives of minimizing the weight of these structures and achieving the desired results with regularity. Key results indicate that DLSA is faster in convergence, gives better quality solutions and is more robust among diverse problem domains. DLSA is an effective and reliable optimization tool that can readily be applied to solve real-world and complex engineering problems.

    Keywords :

    Dynamic Leader Sibha Algorithm , Metaheuristic Optimization , Engineering Design Problems , Exploration and Exploitation , High-Dimensional Search Spaces

    References

    [1] K. S. Guedes, C. F. de Andrade, P. A. C. Rocha, R. dos S. Mangueira, and E. P. de Moura. Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions. Applied Energy, 268:114952, 2020.

    [2] A. Mohammadzadeh, M. Masdari, F. S. Gharehchopogh, and A. Jafarian. A hybrid multi-objective metaheuristic optimization algorithm for scientific workflow scheduling. Cluster Computing, 24(2):1479– 1503, 2021.

    [3] M. Dehghani and H. Samet. Momentum search algorithm: A new meta-heuristic optimization algorithm inspired by momentum conservation law. SN Applied Sciences, 2(10):1720, 2020.

    [4] M. Cikan and B. Kekezoglu. Comparison of metaheuristic optimization techniques including equilibrium optimizer algorithm in power distribution network reconfiguration. Alexandria Engineering Journal, 61(2):991–1031, 2022.

    [5] J. Zhou, X. Shen, Y. Qiu, X. Shi, and M. Khandelwal. Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms. Tunnelling and Underground Space Technology, 126:104570, 2022.

    [6] F. N. Al-Wesabi, M. Obayya, M. A. Hamza, J. S. Alzahrani, D. Gupta, and S. Kumar. Energy aware resource optimization using unified metaheuristic optimization algorithm allocation for cloud computing environment. Sustainable Computing: Informatics and Systems, 35:100686, 2022.

    [7] F. A. Hashim, K. Hussain, E. H. Houssein, M. S. Mabrouk, and W. Al-Atabany. Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Intelligence, 51(3):1531–1551, 2021.

    [8] M. Hamza Zafar, N. Mujeeb Khan, A. Feroz Mirza, M. Mansoor, N. Akhtar, M. Usman Qadir, N. Ali Khan, and S. K. Raza Moosavi. A novel meta-heuristic optimization algorithm based mppt control technique for pv systems under complex partial shading condition. Sustainable Energy Technologies and Assessments, 47:101367, 2021.

    [9] M. Dehghani and H. Samet. Momentum search algorithm: A new meta-heuristic optimization algorithm inspired by momentum conservation law. SN Applied Sciences, 2(10):1720, 2020.

    [10] A. H. Halim, I. Ismail, and S. Das. Performance assessment of the metaheuristic optimization algorithms: An exhaustive review. Artificial Intelligence Review, 54(3):2323–2409, 2021.

    [11] 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.

    [12] H. Tran-Ngoc, S. Khatir, H. Ho-Khac, G. De Roeck, T. Bui-Tien, and M. Abdel Wahab. Efficient artificial neural networks based on a hybrid metaheuristic optimization algorithm for damage detection in laminated composite structures. Composite Structures, 262:113339, 2021.

    [13] M. A. Hamza J. S. Alzahrani D. Gupta F. N. Al-Wesabi, M. Obayya and S. Kumar. Energy aware resource optimization using unified metaheuristic optimization algorithm allocation for cloud computing environment. Sustainable Computing: Informatics and Systems, 35:100686, 2022.

    [14] S. Kamyab and M. Eftekhari. Feature selection using multimodal optimization techniques. Neurocomputing, 171:586–597, 2016.

    [15] N. Khodadadi, E. Khodadadi, Q. Al-Tashi, E.-S. M. El-Kenawy, L. Abualigah, S. J. Abdulkadir, A. Alqushaibi, and S. Mirjalili. Baoa: Binary arithmetic optimization algorithm with k-nearest neighbor classifier for feature selection. IEEE Access, 11:94094–94115, 2023.

    [16] Y. Liu, G.Wang, H. Chen, H. Dong, X. Zhu, and S.Wang. An improved particle swarm optimization for feature selection. Journal of Bionic Engineering, 8(2):191–200, 2011.

    [17] M. Mafarja and S. Mirjalili. Whale optimization approaches for wrapper feature selection. Applied Soft Computing, 62:441–453, 2018.

    [18] J. P. Papa, G. H. Rosa, A. N. de Souza, and L. C. S. Afonso. Feature selection through binary brain storm optimization. Computers & Electrical Engineering, 72:468–481, 2018.

    [19] X. Song, Y. Zhang, D. Gong, and X. Sun. Feature selection using bare-bones particle swarm optimization with mutual information. Pattern Recognition, 112:107804, 2021.

    [20] S. Tabakhi, P. Moradi, and F. Akhlaghian. An unsupervised feature selection algorithm based on ant colony optimization. Engineering Applications of Artificial Intelligence, 32:112–123, 2014.

    [21] H. M. Zawbaa, E. Emary, B. Parv, and M. Sharawi. Feature selection approach based on moth-flame optimization algorithm. In 2016 IEEE Congress on Evolutionary Computation (CEC), pages 4612–4617, 2016.

    [22] B. Abdollahzadeh, F. S. Gharehchopogh, N. Khodadadi, and S. Mirjalili. Mountain gazelle optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems. Advances in Engineering Software, 174:103282, 2022.

    [23] V. P. Singh A. K. Sangaiah and M. Shojafar. Metaheuristic algorithms in industry 4.0: Recent advances, issues, and applications. Computers Electrical Engineering, 89:106903, 2020.

     

    Cite This Article As :
    M., El-Sayed. , Ali, Amel. , Sami, Doaa. , H., Amal. , A., Sarah. , A., Abdelaziz. , Ibrahim, Abdelhameed. , M., Marwa. Dynamic Leader Sibha Algorithm (DLSA): A Novel Hierarchical Metaheuristic Approach for Solving Engineering Design Problems. Journal of Cybersecurity and Information Management, vol. , no. , 2025, pp. 120-133. DOI: https://doi.org/10.54216/JCIM.160110
    M., E. Ali, A. Sami, D. H., A. A., S. A., A. Ibrahim, A. M., M. (2025). Dynamic Leader Sibha Algorithm (DLSA): A Novel Hierarchical Metaheuristic Approach for Solving Engineering Design Problems. Journal of Cybersecurity and Information Management, (), 120-133. DOI: https://doi.org/10.54216/JCIM.160110
    M., El-Sayed. Ali, Amel. Sami, Doaa. H., Amal. A., Sarah. A., Abdelaziz. Ibrahim, Abdelhameed. M., Marwa. Dynamic Leader Sibha Algorithm (DLSA): A Novel Hierarchical Metaheuristic Approach for Solving Engineering Design Problems. Journal of Cybersecurity and Information Management , no. (2025): 120-133. DOI: https://doi.org/10.54216/JCIM.160110
    M., E. , Ali, A. , Sami, D. , H., A. , A., S. , A., A. , Ibrahim, A. , M., M. (2025) . Dynamic Leader Sibha Algorithm (DLSA): A Novel Hierarchical Metaheuristic Approach for Solving Engineering Design Problems. Journal of Cybersecurity and Information Management , () , 120-133 . DOI: https://doi.org/10.54216/JCIM.160110
    M. E. , Ali A. , Sami D. , H. A. , A. S. , A. A. , Ibrahim A. , M. M. [2025]. Dynamic Leader Sibha Algorithm (DLSA): A Novel Hierarchical Metaheuristic Approach for Solving Engineering Design Problems. Journal of Cybersecurity and Information Management. (): 120-133. DOI: https://doi.org/10.54216/JCIM.160110
    M., E. Ali, A. Sami, D. H., A. A., S. A., A. Ibrahim, A. M., M. "Dynamic Leader Sibha Algorithm (DLSA): A Novel Hierarchical Metaheuristic Approach for Solving Engineering Design Problems," Journal of Cybersecurity and Information Management, vol. , no. , pp. 120-133, 2025. DOI: https://doi.org/10.54216/JCIM.160110