An Enhanced Whale Optimization Algorithm Based on Fibonacci Search Principle for Service Composition in the Internet of Things

Service composition in the Internet of Things (IoT) poses significant challenges owing to the dynamics in IoT ecosystems and the exponential increase in service candidates. This paper proposes an Enhanced Whale Optimization Algorithm (EWOA) by introducing the Fibonacci search principle for service composition optimization to overcome certain shortcomings of conventional approaches, including slow convergence and being stuck in local optima, in addition to imbalanced exploration-exploitation trade-offs. The proposed EWOA combines the application of nonlinear crossover weights with a Fibonacci search to optimize the global exploration and local exploitation searches of the basic version, thereby producing a better solution. Several simulations were performed for IoT functions. Among the experiments involving different QOS-based service compositions, the results show that the EWOA achieves superior and faster convergence capability with enhanced convergence compared to recent methods.

@article{CUI2025,
  title     = {An Enhanced Whale Optimization Algorithm Based on Fibonacci Search Principle for Service Composition in the Internet of Things},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yun CUI},
  doi       = {10.14569/IJACSA.2025.0160292},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160292}
}