露天矿运输调度问题求解的自适应果蝇优化算法

针对露天矿运输调度问题,提出了一种改进的果蝇优化算法。首先,对露天矿运输调度问题进行了分析,以矿石开采能力和运输能力为约束条件,以运输成本最小为目标函数,建立了露天矿运输调度问题的数学模型。然后,该算法根据果蝇的味道浓度自适应调整搜索步长,同时满足了算法的全局寻优能力和局部寻优能力。最后,将自适应果蝇优化算法应用于求解实际的露天矿运输调度问题,与标准果蝇优化算法和遗传算法进行实验比较分析,给出了优化结果。实验结果表明:该算法的收敛精度和收敛速度均优于其他算法,证明了该算法可以有效求解露天矿运输调度问题。

Adaptive Fruit Fly Optimization Algorithm for Solving Open-pit Hauling Dispatching Optimization Problem

In order to solve the open-pit hauling dispatching problem, an adaptive fruit fly optimization algorithm (AFOA) is proposed. First of all,based on analyzing the open-pit hauling dispatching problem,a mathematical model of the open-pit hauling dispatching is established,which takes the quantity of ore and transport as constraints,and the minimum trans-portation cost as the objective function. Then,the adaptive step-size search is adopted by the smell concentration value,which satisfies the global optimization capability and local optimization capability. Finally,the adaptive fruit fly optimization algorithm is used to solve the practical open-pit hauling dispatching problem. Compared with standard fruit fly optimization algorithm ( FOA) and genetic algorithm ( GA) ,the optimal result is obtained. The experimental results show that AFOA has better ad-vantages of convergence accuracy and rate,and AFOA can solve open-pit hauling dispatching problem effectively.