Improved gray wolf optimization algorithm for solving low-carbon transportation scheduling problem in open-pit mines
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摘要: 针对露天矿低碳运输调度问题,以采矿场开采量、破碎场破碎量及卡车数量为约束条件,以碳排费用和运输费用之和最小为目标函数,建立了露天矿低碳运输调度问题的数学模型。针对灰狼优化算法用于求解露天矿低碳运输调度问题时容易陷入局部最优的问题,提出了一种改进的灰狼优化算法。该算法在灰狼优化算法中引入迁移操作,并且根据灰狼的适应度函数值动态地修正其迁移概率,有利于跳出局部最优,较快地寻找到全局最优,有效均衡了全局寻优能力和局部寻优能力。实验结果表明,该算法具有较高的寻优精度和较快的寻优速度,利用该算法对露天矿低碳运输调度进行优化后,提高了运输效率,减少了碳排费用和运输费用。Abstract: 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.
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