Intelligent decision-making for mine airflow on demand based on the improved artificial bee colony algorithm

To address the issue of slow convergence speed in solving the unconstrained optimization mathematical model of mine airflow control using existing metaheuristic algorithms, an intelligent decision-making method for mine airflow on demand based on an improved Artificial Bee Colony (ABC) algorithm was proposed. The decision variable was the mine ventilation branch resistance adjustment, with the constraint that the actual airflow in each branch matched the required airflow. The objective was to minimize the difference between the target branch airflow and the ideal airflow. A mine airflow on-demand control intelligent decision-making model was established. The Lagrange relaxation method was used to optimize the model's constraint conditions, the conflict count method was used to optimize the objective function of the model, and random search methods combined with heuristic algorithms were used to optimize the search strategy of the model. To address the issue of insufficient utilization capability in the ABC algorithm, an improved ABC algorithm was proposed and applied to solve the mine airflow on-demand control intelligent decision-making model. The algorithm introduced the population's historical optimal solution to guide the foraging behavior of the honeybees during local optimization and used a general reverse learning strategy to preserve the scout bees' search experience, which effectively balanced the exploration and exploitation capabilities of the algorithm. Experimental results showed that, compared with the Particle Swarm Optimization (PSO) algorithm, the ABC algorithm, the Gbest-guided artificial bee colony (GABC) algorithm, and artificial bee colony with generalized opposition-based learning (ABC-GOBL) algorithm, the improved ABC algorithm could solve the optimal solution of mine airflow on-demand control more quickly and stably, with an airflow control accuracy of up to 0.49 m3/s.