Research on Intellectualized Location of Coal Gangue Logistics Nodes Based on Particle Swarm Optimization and Quasi-Newton Algorithm

The optimization of integrated coal gangue system of mining, dressing and backfilling in deep underground mining is a multi-objective and complex decision-making process, and the factors such as spatial layout, node location, transportation equipment need to be considered comprehensively. In order to realize the intellectualized location of the nodes of the logistics and transportation system of underground mining and dressing coal and gangue, this paper establishes the model of the logistics and transportation system of underground mining and dressing coal gangue, and analyzes the key factors of the intellectualized location of the logistics and transportation system of coal and gangue. In order to solve the problems of complex iterative update, slow running speed and poor stability of output results when particle swarm optimization (PSO) algorithm is used to solve the problem of node location, this paper proposes a particle swarm optimization and quasi-Newton algorithm (PSO-QNMs) for intellectualized node location of coal gangue system. By using MATLAB, this paper compares the calculation results of PSO algorithm and PSO-QNMs algorithm. The experimental results show that PSO-QNMs algorithm reduces the complexity of the calculation, increases the computational efficiency by 8 times, saves 42.8% of the cost, and improves the node optimization efficiency of the mining, dressing and backfilling system under the complex underground environment. Combined with the specific conditions of Xinjulong coal mine, the key nodes of underground coal and gangue system are located intelligently. The results prove that the method has high convergence speed and solving accuracy, which provides a basis for the optimization of mine logistics system in underground mining.