基于IDE算法的金属矿山技术指标整体动态优化研究及应用

Research and Application on Overall Dynamic Optimization of Metal Mine Technical Indicators Based on IDE Algorithm

为更好地利用金属矿产资源,对矿山技术指标进行整体动态优化研究。首先,分别采用核密度估计方法、BP神经网络和指数回归方法拟合技术指标关系模型;然后,在此基础上,构建了整体动态优化模型,并提出相应的改进差分进化算法;最后,将所建立的关系模型、优化模型和优化算法应用于银山铜矿。结果表明:所建立的关系模型拟合效果好,具有较高应用价值;优化结果符合矿山实际情况,验证了模型和算法的有效性,且对矿山生产和计划具有指导作用。

In order to make better use of metal mineral resources,the overall dynamic optimization study of mine technology indexes was carried out.Firstly,the kernel density estimation method,BP neural network and exponential regression method were used to fit the relationship model of technical indicators respectively.Then,based on this,the overall dynamic optimization model was constructed,and the corresponding improved differential evolution algorithm was proposed.Finally,the established relationship model,optimization model and optimization algorithm were applied to Yinshan Copper Mine.The results show that the established relationship model has good fitting effect and high application value.The optimization results are in line with the actual situation of the mine,which verifies the effectiveness of the model and algorithm,and has a guiding role in mine production and planning.