Improved gray wolf optimization algorithm for solving low-carbon transportation scheduling problem in open-pit mines

In order to solve the problem of low-carbon transportation scheduling in open-pit mines, the mathematical model is established by taking the mining volume, crushing volume of crushing stations and the number of trucks as constraints and taking the minimum sum of carbon emission cost and transportation cost as the objective function. An improved gray wolf optimization algorithm is proposed for the problem that gray wolf optimization algorithm is easy to fall into local optimum when it is used to solve the low-carbon transportation scheduling problem of open-pit mines. The algorithm introduces migration operation in the gray wolf optimization algorithm and dynamically modifies the migration probability of the gray wolf optimization algorithm according to its fitness function value. It is beneficial to go beyond the local optimum and obtain the global optimum faster so as to effectively balance the global optimization ability and local optimization ability. Experimental results show that the algorithm has higher optimization accuracy and faster optimization speed. By applying this algorithm to optimize low-carbon transportation scheduling in open-pit mines, transportation efficiency has been improved and carbon emissions and transportation costs have been reduced.