Intelligent decision-making for mine airflow on demand based on the improved artificial bee colony algorithm
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摘要:
针对现有元启发式算法求解矿井风量调控无约束优化数学模型存在收敛速度较慢的问题，提出了一种基于改进人工蜂群算法（ABC）的矿井风量按需调控智能决策方法。以矿井调节分支风阻为决策变量、各分支实际风量与需风量相符合为约束条件，以目标用风分支风量与理想风量差距最小为目标，建立了矿井风量按需调控智能决策模型；运用拉格朗日松弛方法优化模型的约束条件，采用冲突数方法优化模型的目标函数，利用随机搜索方法和启发式算法优化模型的搜索策略。针对人工蜂群算法（ABC）利用能力不足的问题，提出了一种改进ABC算法，并将其用于求解矿井风量按需调控智能决策模型。该算法在采蜜蜂局部寻优时引入群体历史最优解引导采蜜行为，并利用一般反向学习策略保存侦查蜂的搜索经验，良好地平衡了算法的探索和利用能力。实验结果表明：与粒子群优化（PSO）算法、ABC算法、基于全局最优的人工蜂群（GABC）算法和基于一般反向学习的人工蜂群（GABC−GOBL）算法相比，改进ABC算法能更加快速、稳定地求解出矿井风量按需调控最优方案，且风量调控精度可达0.49 m3/s。
Abstract: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.
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表 1 通风网络初始数据
Table 1 Ventilation network initial data
分支
编号风阻/
（N·s2·m−8）风量/
（m3·s−1）分支
编号风阻/
（N·s2·m−8）风量/
（m3·s−1）1 0.006455 129.50 9 0.114862 12.32 2 0.004709 29.96 10 0.085501 14.27 3 0.009727 20.85 11 0.001561 62.09 4 0.001339 73.38 12 0.001391 73.38 5 1.572439 5.31 13 0.015521 50.81 6 0.001562 62.10 14 0.005727 129.50 7 0.232000 11.28 15 0 129.50 8 0.013821 35.51 
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表 2 通风网络各分支类型、风阻和风量调节范围
Table 2 Branch types, air resistance, and airflow regulation range of the ventilation network
分支编号 分支类型 风阻调节范围/（N·s2·m−8） 风量调节范围/（m3·s−1） 1 一般分支 0.006455 129.50 2 其他用风分支 0.004709 26.96～32.96 3 其他用风分支 0.009727 18.77～22.94 4 一般分支 0.001339 3.94～126.00 5 一般分支 1.572439 3.94～126.00 6 一般分支 0.001562 3.94～126.00 7 其他用风分支 0.040940 ～0.884324 10.15～12.41 8 一般分支 0.013821 ～0.844314 3.94～126.00 9 其他用风分支 0.016615 ～0.577616 11.09～13.55 10 目标用风分支 0.085501 31.50～32.50 11 一般分支 0.001561 3.94～126.00 12 一般分支 0.001391 3.94～126.00 13 一般分支 0.004964 ～0.714323 3.94～126.00 14 其他用风分支 0.005727 129.50 15 虚拟分支 0 129.50
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表 3 不同算法下模型求解结果
Table 3 Model solution results under different algorithms
算法 算法成功率/% 平均收敛代数 平均寻优时间/s 改进ABC 100 42 132.72 GABC 87 53 167.48 ABC−GOBL 100 122 385.52 ABC 100 158 516.68 PSO 0 － －
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表 4 改进ABC算法求解出的最优矿井风阻调控方案
Table 4 Optimal mine ventilation resistance control scheme solved by the improved artificial bee colony algorithm
分支
编号风阻/
（N·s2·m−8）风量/
（m3·s−1）分支
编号风阻/
（N·s2·m−8）风量/
（m3·s−1）1 0.006455 129.50 9 0.571217 12.21 2 0.004709 27.25 10 0.085501 31.51 3 0.009727 18.96 11 0.001561 64.23 4 0.001339 74.83 12 0.001391 74.83 5 1.572439 8.49 13 0.051381 46.21 6 0.001562 64.23 14 0.005727 129.50 7 0.871307 10.60 15 0 129.50 8 0.209715 20.51
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