矿井作业环境复杂,各类地质灾害以及水害极易影响井下安全生产,因而预先对灾害发生时的人员逃生路径进行规划很有必要.为获取矿井最短逃生路线,提出了一种改进灰狼优化算法的路径规划方法.该方法针对灰狼优化算法(Grey Wolf Optimization,GWO)早熟收敛和易陷入局部最优解的不足,提出了一种基于Logistic映射和Tent映射组合的改进灰狼算法(LT-GWO),提高其全局搜索能力.结合矿井实际工作环境,将改进算法应用于井下逃生路径规划,并通过设定合理路径约束和限制条件,获得了较好的路径规划结果.研究表明:所提算法在平均路径长度、路径长度标准差、平均迭代次数和平均寻优耗时等指标上显著优于已有算法,并且具有较好的鲁棒性.所提算法对于矿井灾害等应急场景下的路径规划问题研究有一定的参考价值.
Study on Mine Shortest Escape Path Planning Based on Improved Grey Wolf Optimization Algorithm
Mine operating environment is complicated,all kinds of geological disasters and water disasters are easy to af-fect underground safety production,so it is necessary to plan the escape path of personnel in advance when disasters occur.In order to obtain the shortest escape route of mine,a path planning method with improved grey Wolf optimization algorithm is pro-posed.Aiming at the shortcomings of Grey Wolf Optimization(GWO),which is precocious convergence and easy to fall into lo-cal extreme values,an improved Grey Wolf Optimization(LT-GWO)algorithm based on the combination of Logistic mapping and Tent mapping is proposed to improve its global search capability.According to the actual working environment of mine,the improved algorithm is applied to underground escape path planning,and the effective path planning results are obtained by set-ting reasonable path constraints and limiting conditions.The results show that the proposed algorithm is significantly better than the existing algorithms on average path length,standard deviation of path length,average number of iterations and average search time,and has good robustness.The proposed algorithm has a certain reference value for the research of path planning under mine disaster and other emergency scenarios.
underground rescuegrey wolf optimization algorithmmine escape path planningLogistic mapTent map
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