A quantum-driven multi-objective scheduler for scalable task orchestration in fog-based cyber-physical-social systems

Fog computing extends cloud capabilities toward the network edge, enabling low-latency Cyber-Physical-Social System (CPSS) services in domains such as smart cities and healthcare. However, multi-objective task scheduling in fog environments remains challenging due to conflicting goals minimizing execution time, resource costs, and energy consumption combined with the scalability limitations of classical evolutionary algorithms, which often converge slowly and produce poorly distributed Pareto fronts in large networks. To address these issues, this paper introduces FOG-QIEA, a quantum-inspired evolutionary algorithm designed for tri-objective fog scheduling. FOG-QIEA augments adaptive neighborhood mechanisms with quantum-inspired operators, including superposition-based population initialization, rotation-gate–driven updates, and measurement-guided selection, enabling faster and more diverse exploration of the solution space. The proposed model jointly optimizes total execution time, cost (including SLA violations), and energy efficiency while maintaining scalability across CPSS deployments with thousands of IoT tasks. Extensive simulations in iFogSim using realistic CPSS scenarios show that FOG-QIEA outperforms NSGA-II, MMPA-based approaches, and classical adaptive fog schedulers by 20–35% in convergence speed, 15–25% in energy reduction, and achieves significantly improved Pareto diversity. These results demonstrate the potential of FOG-QIEA as a sustainable and efficient scheduling framework, supporting future advancements toward quantum-hybrid optimization in fog and edge networks.