Experimental Study on Optimization of Transportation Path in Open Pit Mines Based on Genetic Ant Colony Reinforcement Learning Algorithm
A mining transportation optimization model based on genetic ant colony reinforcement learning algorithm is proposed to address the problem of easily getting stuck in local optimal solutions when planning paths for complex mining terrain.This model combines the advantages of genetic ant colony algorithm and reinforcement learning algorithm to achieve efficient optimization of mining transportation paths.Firstly,using the genetic algorithm to efficiently search the solution space and generate an initial set of transportation paths to explore a broader solution space.Secondly,introducing the ant colony algorithm to discover the optimal solution during the search process,which is the optimal transportation path.Finally,the reinforcement learning algorithm was introduced to simulate the decision-making process of intelligent agents and adjust transportation paths based on environmental feedback,so that the model can learn better decision-making strategies and can gradually improve transportation efficiency through continuous iteration.Experimental verification shows that compared to traditional genetic ant colony algorithm,the shortest transportation distance obtained by the genetic ant colony reinforcement learning algorithm has been reduced by 20.06％,transportation cost has been reduced by 12.60％,and the time to find the optimal path has been reduced by 51.55％.This optimal model can improve transportation efficiency,reduce transportation cost,and provide efficient and environmentally friendly solutions for mining transportation.
Transportation of open pit mineGenetic ant colony reinforcement learning algorithmPath planningTransportation cost
万方数据
维普
