Optimization Algorithms for Solving Large Scale Engineering ‎Problems

  • Authors

    • Damanjeet Aulakh Center of Research Impact and Outcome, Chitkara University, Rajpura, Punjab, India
    • Sudhanshu Dev Chitkara Center for Research and Development, Chitkara University, Himachal Pradesh, India
    • Anmol Yadav Assistant Professor, Maharishi School of Engineering & Technology, Maharishi University of Information Technology, Lucknow, ‎India
    • Dr. Swarna Swetha Kolaventi Assistant Professor, uGDX, ATLAS SkillTech University, Mumbai, India
    • Dr. Narayan Patra Associate Professor, Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, ‎Odisha, India
    • Dr. R. M. Joany Associate Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, ‎Chennai, Tamil Nadu, India
    • Vinay Kumar ‎Sadolalu Boregowda Assistant Professor, Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, JAIN ‎‎(Deemed-to-be University), Ramnagar District, Karnataka, India
    https://doi.org/10.14419/298ffy09

    Received date: May 2, 2025

    Accepted date: May 31, 2025

    Published date: October 31, 2025

  • Optimization; Engineering Problems; Efficiency

  • Abstract

    Global optimization helps determine a problem's optimal value. Exploration and exploitation are utilized in the optimization process to ‎explore and refine the solution space. Intense exploitation accelerates the rate at which agents converge to the global optimum, whereas a ‎strong exploration capability is necessary to navigate the multi-modal search spaces. Based on the idea of mass attraction, the Gravitational ‎Search Algorithm (GSA) is a very promising heuristic algorithm that draws inspiration from physics. For real-world situations, it has been ‎routinely employed to determine the global optima. Due to its inherent strong exploration capabilities and greater diversification power, GSA ‎is less likely to experience local minima dropping.‎

    Additionally, GSA stores the best candidate solutions using an elitist approach. Thus, it shields optimal agents from interference from local ‎minima. However, when tackling complex problems, GSA encounters obstacles such as premature convergence and entrapment in local ‎minima‎.

  • References

    1. Ali, A. F., & Tawhid, M. A. (2017). A hybrid particle swarm optimization and genetic algorithm with population partitioning for large-scale optimiza-tion problems. Ain Shams Engineering Journal, 8(2), 191–206. https://doi.org/10.1016/j.asej.2016.07.008.
    2. Nemhauser, G. L. (1994). The age of optimization: Solving large-scale real-world problems. Operations Research, 42(1), 5–13. https://doi.org/10.1287/opre.42.1.5.
    3. El-Saadawi, E., Abohamama, A. S., & Alrahmawy, M. F. (2024). IoT-based optimal energy management in smart homes using the harmony search optimization technique. International Journal of Communication and Computer Technologies, 12(1), 1–20.
    4. Liu, H., Wang, Y., Tu, L., Ding, G., & Hu, Y. (2019). A modified particle swarm optimization for large-scale numerical optimizations and engineering design problems. Journal of Intelligent Manufacturing, 30, 2407–2433. https://doi.org/10.1007/s10845-018-1403-1.
    5. Prashant, P. M., Kumaraswamy, B., & Sardana, S. (2025). Compact Antenna Designs for Ultra-Connected IoT Devices. National Journal of Antennas and Propagation, 7(2), 19-23. https://doi.org/10.31838/NJAP/07.02.04.
    6. Kumar, P. S., Rajan, C., Gopinath, R., Gnanabaskaran, A., & Vijaisai, R. (2024). Energy-efficient clustering and routing algorithm using ant lion op-timization in WSN. In International Conference on Next Generation Electronics. https://doi.org/10.1109/NEleX59773.2023.10420921.
    7. Li, Y., Yu, X., & Liu, J. (2022). Enhanced butterfly optimization algorithm for large-scale optimization problems. Journal of Bionic Engineering, 19(2), 554–570. https://doi.org/10.1007/s42235-021-00143-3.
    8. Shahriar, A. Z. M., Atiquzzaman, M., & Ivancic, W. D. (2011). Evaluation of the route optimization for NEMO in satellite networks. Journal of Wire-less Mobile Networks, Ubiquitous Computing, and Dependable Applications, 2(2), 46–66.
    9. Tsurkov, V. (2013). Large-scale optimization: Problems and methods (Vol. 51). Springer Science & Business Media.
    10. Alamer, L., & Shadadi, E. (2023). DDoS attack detection using long-short term memory with bacterial colony optimization on IoT environment. Journal of Internet Services and Information Security, 13(1), 44–53. https://doi.org/10.58346/JISIS.2023.I1.005.
    11. Akay, B., & Karaboga, D. (2012). Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing, 23, 1001–1014. https://doi.org/10.1007/s10845-010-0393-4.
    12. Yağız, E., Ozyilmaz, G., & Ozyilmaz, A. T. (2022). Optimization of graphite-mineral oil ratio with response surface methodology in glucose oxidase-based carbon paste electrode design. Natural and Engineering Sciences, 7(1), 22–33. https://doi.org/10.28978/nesciences.1098655.
    13. Gallagher, K., & Sambridge, M. (1994). Genetic algorithms: A powerful tool for large-scale nonlinear optimization problems. Computers & Geosci-ences, 20(7–8), 1229–1236. https://doi.org/10.1016/0098-3004(94)90072-8.
    14. Long, W., Jiao, J., Liang, X., & Tang, M. (2018). Inspired grey wolf optimizer for solving large-scale function optimization problems. Applied Math-ematical Modelling, 60, 112–126. https://doi.org/10.1016/j.apm.2018.03.005.
    15. Papadrakakis, M., Lagaros, N. D., Tsompanakis, Y., & Plevris, V. (2001). Large scale structural optimization: Computational methods and optimiza-tion algorithms. Archives of Computational Methods in Engineering, 8, 239–301. https://doi.org/10.1007/BF02736645.
    16. Afshar, M. H. (2012). Large scale reservoir operation by constrained particle swarm optimization algorithms. Journal of Hydro-Environment Re-search, 6(1), 75–87. https://doi.org/10.1016/j.jher.2011.04.003.
    17. Soria, F., & Caddwine, H. (2025). Deep reinforcement learning-based power electronics control for high-efficiency solar and wind systems. National Journal of Renewable Energy Systems and Innovation, 1(4), 26–33.
    18. Abdullah, D. (2025). Optimal sizing and control of microgrid-integrated renewable energy systems using deep reinforcement learning. National Jour-nal of Intelligent Power Systems and Technology, 1(2), 10–17.
    19. Mpamije, L. J., & Freire, G. F. (2025). SiC and GaN device-based power converters for high-efficiency drive applications. National Journal of Elec-tric Drives and Control Systems, 1(3), 1–9.
    20. David, G., & Vana, C. (2025). Loss minimization and thermal optimization in power converters for solar applications. Transactions on Power Elec-tronics and Renewable Energy Systems, 1(3), 15–22.
    21. Barhoumi, E. M., & Luedkea, R. H. (2025). Bidirectional charging strategies for vehicle-to-grid integration using smart storage systems. Transactions on Energy Storage Systems and Innovation, 1(4), 18–25.
    22. Getachew, B., & Alabi, D. (2025). AI-optimized design and advanced control strategies for high-efficiency electrical machines in sustainable power conversion systems. National Journal of Electrical Machines & Power Conversion, 1(2), 9–15.
    23. Usikalua, M. R., & Unciano, N. (2025). Mathematical modeling of epidemic dynamics: Integrating public health and data science. Bridge: Journal of Multidisciplinary Explorations, 1(1), 11-22.
    24. Kigarura, M., & Jakhir, C. (2025). A scalable parallel processing architecture for real-time and energy-efficient biomedical signal analysis in edge-enabled health monitoring systems. Journal of Integrated VLSI, Embedded and Computing Technologies, 2(3), 56–62.
    25. Salameh, A. A., & Ye, K. M. (2025). Design and Optimization of Coarse-Grained Reconfigurable Array (CGRA) Architecture for Efficient Pro-cessing-in-Memory (PIM) Systems. Journal of VLSI Circuits and Systems, 7(1), 11-18. https://doi.org/10.31838/jvcs/07.01.02.

  • Downloads

  • How to Cite

    Aulakh , D. ., Dev , S. ., Yadav , A. ., Kolaventi , D. S. S. ., Patra , D. N. ., Joany , D. R. M. ., & Boregowda , V. K. ‎Sadolalu . (2025). Optimization Algorithms for Solving Large Scale Engineering ‎Problems. International Journal of Basic and Applied Sciences, 14(SI-1), 325-331. https://doi.org/10.14419/298ffy09