Hybrid Optimization Method for Service Deployment in IoT using Salp Swarm Optimization and Simulated Annealing Algorithms

Efficient and reliable service deployment remains a critical challenge in the Internet of Things (IoT) due to the highly dynamic nature of user demands, device heterogeneity, and network constraints. To address this issue, this paper presents a Hybrid Optimization Method (HOM) that integrates Salp Swarm Optimization (SSO) with Simulated Annealing (SA) for adaptive and balanced service placement. The proposed HOM leverages the exploration ability of SSO to determine optimal initial service distribution, while SA refines the placement through iterative verification to prevent overload and minimize delay. This hybrid mechanism ensures stable load distribution and sustained service responsiveness under varying user densities and request rates. Experimental results demonstrate that HOM significantly improves IoT service deployment, achieving upto 11.28% lower response time, 11.72% higher service response, and 11.8% better load handling compared with existing optimization models. The proposed method offers a scalable and efficient solution for real-time IoT service management and deployment optimization.