Enhanced Hybrid Spider Monkey Optimization with Particle Swarm Optimization for Resource Management in Fog Computing Environment

The rapid expansion of Internet of Things (IoT) applications has driven the widespread adoption of fog computing, which places computational resources closer to data sources. However, achieving efficient resource management in fog computing environments remains a significant challenge due to diverse resource constraints, dynamic workload variations, and stringent Quality of Service (QoS) requirements. This paper introduces a novel Enhanced Hybrid Spider Monkey Optimization (EH-SMO) algorithm with Particle Swarm Optimization (PSO) for task scheduling in fog computing, inspired by the social foraging behavior of spider monkeys and enhanced by PSO's efficient local search capabilities. The proposed EH-SMO algorithm dynamically allocates computational resources, balances workloads across fog nodes, and optimizes makespan, energy consumption, and resource utilization objectives through adaptive parameter control for energy efficient and low latency performance. Through comprehensive simulation experiments comparing EH-SMO with MCT-SMO and MPSO baseline schemes, the proposed algorithm achieves substantial improvements in scheduling efficiency, energy consumption, and resource allocation by achieving 32% and 17% reduction in makespan, reducing energy consumption by up to 33% and 45%, while improving resource utilization by 18% and 37%, respectively. EH-SMO exhibits robust adaptability to dynamic workload variations and demonstrates superior scalability across different fog computing scenarios, making it a promising solution for real-world IoT applications