矿井通风网络的递阶文化优化方法

Mine ventilation network optimization adopting hierarchy cultural algorithm

针对矿井通风网络优化问题,借鉴文化算法的双层进化结构,提出一种递阶文化算法.算法种群空间采用分层遗传算法,引入递阶编码描述通风网络支路结构,并给出相应的不可行支路修复算子和可行调节支路阻值调节算子;信度空间采用统计学习方法提取公共优势支路作为知识,指导种群空间进化过程中的可调支路选取.仿真结果表明,该算法获得的最优控风方案满足控风要求,且所得方案的加阻值总和最小,所需调风成本更低.

Aiming at mine ventilation network optimization problems, a hierarchy cultural algorithm is proposed by adapting dual structure of cultural algorithm. In the population space, the hierarchy genetic algorithm is adapted. Hierarchy code method is introduced to describe the branch structure of ventilation network. The repair operator for infeasible branches and the regulation operator for feasible branches＇ resistance are given. In the belief space, common dominating branches are extracted as knowledge by statistical learning method and used to guide the selection of adjustive branches in the evolution. Simulation results show that the optimal scheme obtained by the proposed algorithm can meet the needs of air control and has the minimum total resistance. That is, it has the less cost for air control than others.