Abstract

sup>Efficient service placement is a critical challenge in large-scale Internet of Things (IoT) environments, where fog computing must balance deployment cost and resource utilization under heterogeneous and dynamic conditions. To address this challenge, this paper proposes a hybrid metaheuristic approach that combines Rat Swarm Optimization (RSO) and Sunflower Optimization (SFO), leveraging the strong global exploration capability of RSO and the efficient local exploitation behavior of SFO. The proposed RSO–SFO framework integrates both strategies within a unified fitness function designed to minimize deployment cost while ensuring efficient allocation of fog resources. Extensive simulation results demonstrate that the proposed hybrid algorithm consistently outperforms state-of-the-art optimization techniques, including Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and the standalone RSO and SFO methods. Specifically, the RSO–SFO approach achieves a fitness improvement of 45.38%, reduces deployment costs by 43.71%, and maintains a high average resource utilization of 78.83%. These results confirm the effectiveness and robustness of the proposed hybrid strategy for optimal service placement in fog-based IoT environments.