Optimizing Data Transmission and Energy Efficiency in Wireless Networks: A Comparative Study of GA, PSO, and Hybrid Approaches

As wireless communication technology evolves, efficient resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA) networks is becoming more important. This study looks at three resource allocation algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and a hybrid approach that combines both. The hybrid algorithm takes advantage of the strengths of both methods to improve data transmission and energy efficiency. Using simulations in MATLAB, the study assesses algorithms based on key metrics such as data rate, energy consumption, and computational complexity. The findings show that the hybrid approach generally performs better than both GA and PSO, especially in maximizing data rates. This research offers useful information for network operators looking to implement effective resource management strategies in practical wireless communication settings.

@article{Solaiman2025,
  title     = {Optimizing Data Transmission and Energy Efficiency in Wireless Networks: A Comparative Study of GA, PSO, and Hybrid Approaches},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Suhare Solaiman},
  doi       = {10.14569/IJACSA.2025.01604109},
  url       = {https://doi.org/10.14569/IJACSA.2025.01604109}
}