The identification of parameters that affect mining is one of the requirements in executive work in this field. Due to the dangers of flyrock, studying the role of the factors that affect it will be useful to control this serious environmental issue of blasting. In this research, using hybrid intelligence techniques, a new guide to investigate the parameters that affect the occurrence and characteristics of flyrock is presented. Hybrid models were improved based on five types of optimization algorithms, namely particle swarm optimization, artificial bee colony, the imperialist competitive algorithm, firefly algorithm (FA), and genetic algorithm. The process of designing the structure of the models was controlled under the fuzzy Delphi method. This filter helps to determine the most important factors that play a key role in the flyrock phenomenon and its accurate prediction. The best optimization technique was selected based on applying two popular performance indices, i.e., the root-mean-square error and coefficient of determination (R²). As a result, the best combination obtained was the FA-artificial neural network (ANN), which was able to provide the best optimization of the weights and biases of the ANN among all the proposed models. In addition, this system showed the lowest network error in the prediction of flyrock compared to other ANN-based models. The new combination (FA-ANN) can be used as a powerful and practical technique to predict the flyrock distance prior to blasting operations.

识别影响采矿的参数是该领域执行工作的要求之一。由于飞石的危险，研究影响飞石的因素的作用将有助于控制爆破这一严重的环境问题。在这项研究中，使用混合智能技术，提出了一个新的指南来研究影响飞石发生和特征的参数。基于五类优化算法对混合模型进行了改进，分别是粒子群优化，人工蜂群，帝国主义竞争算法，萤火虫算法（FA）和遗传算法。模型结构的设计过程由模糊德尔菲法控制。该过滤器有助于确定在飞石现象及其准确预测中起关键作用的最重要因素。基于应用两个流行的性能指标（即均方根误差和确定系数（R ²）。结果，获得的最佳组合是FA人工神经网络（ANN），它能够在所有提议的模型中对ANN的权重和偏差提供最佳的优化。此外，与其他基于ANN的模型相比，该系统在飞石的预测中显示出最低的网络误差。新的组合（FA-ANN）可作为一种强大而实用的技术来预测爆破作业之前的飞石距离。