Quantum adaptive clonal genetic algorithm for low-energy clustering in agricultural WSNs

Agricultural Wireless Sensor Networks (AWSNs) are essential for real-time monitoring in precision farming, yet their lifetime is severely constrained by limited node energy and the difficulty of battery replacement in large-scale deployments. This study proposes a Quantum Adaptive Clonal Genetic Algorithm (QACGA) to achieve energy-efficient clustering in AWSNs. The algorithm combines quantum-inspired adaptive operators with dynamic adjustments in cluster-head selection, mutation, and cloning rates, while integrating multi-objective constraints related to node distribution, residual energy, and communication distance. Simulation results demonstrate that QACGA consistently reduces energy consumption compared with established clustering algorithms, achieving savings of up to 38.1% relative to PSO, SFLA, and WOA, and also surpassing MRCH under equivalent conditions. In addition to lowering energy costs, QACGA improves clustering stability and extends overall network lifetime across diverse deployment scales. These findings highlight QACGA as a robust and practical optimization framework, providing new benchmarks for energy management in AWSNs and offering valuable insights for smart agriculture applications.