HGWO-RL: Hybrid Grey Wolf Optimization with Q-Learning Refinement for Energy-Aware Multi-Objective Task Scheduling in Heterogeneous Fog–Cloud Networks
Abstract Energy efficiency in fog-cloud IoT task scheduling has become a pressing research challenge as the number of connected devices escalates toward 75 billion. Existing metaheuristic schedulers-including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and the vanilla G...