基于混合粒子群优化算法的矿山生产配矿

发布时间：2019-01-24 17:45

【摘要】：为了提高矿山低品位矿产资源的利用率,确保生产过程中矿石质量的稳定性和均匀性,建立了一种矿山多目标配矿优化模型,并将标准遗传算法中的交叉和变异操作与标准粒子群算法融合,提出求解该优化模型的混合粒子群算法。以国内某地下铝土矿为例,分别运用混合粒子群优化算法、标准遗传算法和标准粒子群算法3种方法对建立的多目标配矿优化模型进行求解,通过对比优化结果发现:混合粒子群优化算法求解的各采区月出矿量完全满足矿山实际生产要求,而标准遗传算法和标准粒子群算法求解结果存在误差,分别达到9.92%和14.94%,且易陷入局部最优值;从迭代进化曲线可知,混合粒子群优化算法收敛速度快,稳定性和鲁棒性较高,具有一定科学研究和实际应用价值。
[Abstract]:In order to improve the utilization ratio of mine low grade mineral resources and ensure the stability and uniformity of ore quality during production, a multi-objective ore matching optimization model was established. Combining the crossover and mutation operations of the standard genetic algorithm with the standard particle swarm optimization algorithm, a hybrid particle swarm optimization algorithm is proposed to solve the optimization model. Taking an underground bauxite mine in China as an example, the multi-objective ore-matching optimization model is solved by using three methods: hybrid particle swarm optimization algorithm, standard genetic algorithm and standard particle swarm optimization algorithm, respectively. By comparing the optimization results, it is found that the monthly ore output of each mining area solved by the hybrid particle swarm optimization algorithm completely meets the actual production requirements of the mine, while the standard genetic algorithm and the standard particle swarm optimization algorithm have errors in solving the results. It reached 9.92% and 14.94%, respectively, and easily fell into local optimum value. It can be seen from the iterative evolution curve that the hybrid particle swarm optimization algorithm has high convergence speed, high stability and robustness, and has certain scientific research and practical application value.
【作者单位】：武汉理工大学资源与环境工程学院;中南大学资源与安全工程学院;
【基金】：中央高校基本科研业务费专项资金资助项目(2017IVA045,2017IVA046) 武汉理工大学自主创新研究基金——交叉学科创新研究项目(2017II35GX)
【分类号】：TD862.5;TP18

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