QIGA: Quantum-Inspired Genetic Algorithm for Dynamic Scheduling in Mobile Edge Computing

This paper introduces a Quantum-Inspired Genetic Algorithm (QIGA) for efficient scheduling in a Mobile Edge Computing (MEC) environment. Inspired by the principles of quantum computing, it enhances solution diversity and convergence in the MEC landscape with heterogeneous resources and dynamic tasks. In this work, the QIGA framework is applied to an optimization problem with multiple conflicting objectives: makespan, energy consumption, and resource utilization. Extensive evaluations demonstrate the effectiveness of our proposed method, which shows improvement over three state-of-the-art scheduling methods in varied burst MEC scenarios in energy consumption about 15.7% on average and up to 39%, 20.5% in makespan, 27.5% in resource utilization, and latency up to 69.9%.