Research on Optimization and Application of Mine Ventilation Based on Improved Cuckoo Search Algorithm

To reduce energy consumption in mine ventilation networks under dynamic air demand, this study proposes an Improved Cuckoo Search (ICS) algorithm for energy-efficient airflow regulation. A nonlinear optimization model is established incorporating airflow balance at nodes, air pressure balance in loops, and fan operating constraints; equality constraints are handled by an exterior penalty function and inequality constraints by an interior penalty function. To overcome standard Cuckoo Search’s insufficient convergence accuracy and premature convergence, adaptive step sizes, an adaptive discovery probability adjustment, and a hybrid update strategy with differential evolution are introduced to enhance global exploration and local exploitation, convergence speed, and stability. The method was validated on an intelligent ventilation platform using field data from the Second Panel of a coal mine in Shaanxi Province. Results show that ICS reduces total power consumption from 482.0 kW to 424.9 kW, achieving an energy-saving rate of 11.85%, and outperforms CS, PSO, and GWO in convergence efficiency, accuracy, and stability. Quantitatively, it converges in 130 iterations (190 for CS, 160 for GWO), with a 0.038 kW objective-function standard deviation (coefficient of variation 0.009%) over 30 runs and 0.22 s per run. The proposed method optimizes airflow distribution while satisfying air-demand constraints, offering a practical approach for energy-efficient mine ventilation.