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 Optimizat...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。展开更多
1 Introduction Underground logistics systems(ULSs)are a set of self-contained,multimodal,and intelligent physical distribution concepts that enable the automated movement of goods via tunnels and underground pipelines...1 Introduction Underground logistics systems(ULSs)are a set of self-contained,multimodal,and intelligent physical distribution concepts that enable the automated movement of goods via tunnels and underground pipelines installed within and between cities(Visser,2018).ULSs are also recognized as the fifth type of logistics and generic supply system after seaways,airlines,roads,and railways(Qian and Guo,2007).展开更多
文摘矿井作业环境复杂,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。
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.72271125 and 71971214).
文摘1 Introduction Underground logistics systems(ULSs)are a set of self-contained,multimodal,and intelligent physical distribution concepts that enable the automated movement of goods via tunnels and underground pipelines installed within and between cities(Visser,2018).ULSs are also recognized as the fifth type of logistics and generic supply system after seaways,airlines,roads,and railways(Qian and Guo,2007).