MOQIEA: A Novel Multiobjective Quantum Immune Evolutionary Algorithm for Nonuniform Clustering Framework in Industrial IoT

With the rapid development of the Industrial Internet of Things (IIoT), managing resources for energy-constrained devices poses a significant challenge. Addressing issues such as IIoT load imbalance, energy holes, and unreliable transmission is crucial for robust network operation and data awareness. This study itroduces a novel nonuniform clustering framework called the Multiobjective Quantum Immune Evolutionary Algorithm (MOQIEA). This algorithm integrates multi-objective factors, including network energy consumption, energy potential field, and residual energy. By combining multi-objective optimization with quantum computing, MOQIEA enhances the sustainable energy operation of IIoT networks. The algorithm employs the quantum state observation mechanism for dynamic cluster-head node selection and adjusts the clustering scheme based on the IIoT topology. An optimal ring spacing scheme is designed to incorporate the energy equalization factor for dynamically selecting nonuniform cluster ring spacing. Three objective functions are introduced: network energy consumption, energy potential field strength, and residual energy. It aims to balance network loads and eliminate energy holes through multi-objective optimization. MOQIEA designed an energy constraint operator to mitigate the premature death of nodes, thus extending network lifetime by reducing the cluster-head election weight of low-energy nodes. Its unique quantum adaptive observation cluster head mechanism improves quantum intervenability and entanglement, balances energy consumption within the clustering scheme, eliminates energy holes, and significantly extends network operational rounds. Various scenarios are set, and simulation experiments are conducted by adjusting network parameters. The results demonstrate that MOQIEA achieves a longer network lifetime, lower network energy consumption, and better data transmission reliability compared to other algorithms.