QIEA-RTO: Quantum-Inspired Evolutionary Algorithm for Resilient Task Offloading in UAV-Assisted IoT Networks

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) has emerged as a promising paradigm to enhance computation and connectivity for mission-critical Internet of Things (IoT) applications. However, efficient task offloading in such networks must jointly consider energy consumption, End-to-End (E2E) delay, and system resiliency under UAV resource constraints, making the problem Non-deterministic Polynomial-time hard (NP-hard). To address this challenge, we propose QIEA-RTO, a Quantum-Inspired Evolutionary Algorithm (QIEA) for Resilient Task Offloading (RTO). QIEA-RTO employs Q-bit encoding, rotation-based evolutionary updates, and repair mechanisms to efficiently solve the joint optimization problem while ensuring (r + 1)-server redundancy for task execution continuity. Simulation results demonstrate that QIEA-RTO achieves lower energy consumption, reduced average E2E delay, higher task success ratio, efficient bandwidth utilization, and robust scalability compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Greedy, and Random baselines. Moreover, QIEA-RTO converges faster than GA, PSO, and Random, highlighting its suitability for dynamic UAV-assisted MEC environments.