The Research Of Optimal Path Planning In Mine Emergency Rescue

In recent years, the number of the mine disaster and mortality rates has declined. However, compared with the advanced countries, mining accident rate is still quite high. The mine laneway radiates in all directions and its environment is varied. After the mine disaster happens, it all involves the problem of path planning in the process of rescuing underground mine personnel and organizing underground staffs away from the disaster area are related to the path planning problem. How to implement disaster prevention quickly and how to make disaster victims stay away from the disaster area quickly is an important standard to the success of the mine emergency rescue.In this paper, ant colony algorithm is applied to mine emergency rescue path planning. According to the problem, it easily trapped into local optimal solution and its search speed is slow, inheres in the basic ant colony algorithm. We propose dual population ant colony algorithm based on the dynamic pheromone updating. This algorithm is based on three different traveling salesman problems as an example and compares with ant system and the max-min ant system by simulation. Simulation results show that this algorithm which searches for the best path average solution is shorter than ant system and max-min ant system. In Oliver30 problem, the average iteration in this algorithm is more than 28% and 12% ant system and the max-min ant system respectively. With the problems scale enlargement, however, the average iteration time in this algorithm is than the ant colony system decreased by 33% and 39% in Eil51 problem and Eil75 problem and is than the max-min ant system decreased by 19% and 18%.The ant colony algorithm based on dynamic pheromone updated double-population experimental validation and analysis by using mine laneway network mode and by changing state transition probability and expectations in this algorithm. Experimental results show that, through setting up reasonable algorithm and the parameters in between the parameters and optimized combination can greatly improve the performance of the algorithm for solving the best path.