Raj Vikram

HGWO-RL: hybrid grey wolf optimization with Q-learning refinement for energy-aware multi-objective task scheduling in heterogeneous fog–cloud networks

Energy-aware task scheduling in fog–cloud IoT networks must balance monetary cost, deadlines and energy. We propose hybrid grey wolf optimizer-reinforcement learning (HGWO-RL), which combines a grey wolf optimizer (GWO) with a tabular Q-learning agent that selects move operators to refine the three elite solutions. The...