The main focus of the present work is to offer an auto-tuning model, called cat swarm optimization (CSO), to predict rock fragmentation. This population-based method has a stochastic formation involving exploration and exploitation phases. CSO is a robust and powerful meta-heuristic algorithm inspired by the behaviors of cats; it is composed of two search modes: seeking and tracing, which can be joined by mixture ratio parameter. CSO is applied to large-scale optimization problems like rock fragmentation to have good forecasting parameters in D80 formulas (D80 is a common descriptor that evaluates rock fragmentation). To evaluate the efficiency of the proposed CSO model, its obtained results were compared to those of the particle swarm optimization (PSO) algorithm. In the modeling, two forms of CSO and PSO models, including power and linear forms, were developed. The comparative results showed that CSO models outperformed the rival in terms of the task defined. The precision of the proposed models was computed using statistical evaluation criteria. Comparison results concluded that CSO-power model with the root mean square error (RMSE) of 0.847 was more computationally efficient with better predictive ability compared to CSO-linear, PSO-linear and PSO-power models with the RMSE of 1.314, 1.545 and 2.307, respectively. Furthermore, the sensitivity analysis revealed the effect of the stemming parameter upon D80 in comparison with other input parameters.

中文翻译：

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一种用于预测岩石破碎的新自动调整模型：猫群优化算法

当前工作的主要重点是提供一种称为猫群优化 (CSO) 的自动调整模型来预测岩石破碎。这种基于人口的方法具有涉及勘探和开发阶段的随机形成。CSO 是一种强大而强大的元启发式算法，其灵感来自猫的行为；它由搜索和跟踪两种搜索模式组成，可以通过混合比参数进行连接。CSO 应用于岩石破碎等大规模优化问题，在 D80 公式中具有良好的预测参数（D80 是评估岩石破碎的常用描述符）。为了评估所提出的 CSO 模型的效率，将其获得的结果与粒子群优化 (PSO) 算法的结果进行了比较。在建模上，CSO和PSO模型有两种形式，包括幂形式和线性形式，被开发。比较结果表明，CSO 模型在定义的任务方面优于竞争对手。使用统计评估标准计算所提出模型的精度。比较结果表明，与均方根误差 (RMSE) 为 1.314、1.545 和 2.307 的 CSO-linear、PSO-linear 和 PSO-power 模型相比，均方根误差 (RMSE) 为 0.847 的 CSO-power 模型计算效率更高，预测能力更好， 分别。此外，敏感性分析揭示了与其他输入参数相比，词干提取参数对 D80 的影响。比较结果表明，与均方根误差 (RMSE) 为 1.314、1.545 和 2.307 的 CSO-linear、PSO-linear 和 PSO-power 模型相比，均方根误差 (RMSE) 为 0.847 的 CSO-power 模型计算效率更高，预测能力更好， 分别。此外，敏感性分析揭示了与其他输入参数相比，词干提取参数对 D80 的影响。比较结果表明，与RMSE为1.314、1.545和2.307的CSO-linear、PSO-linear和PSO-power模型相比，均方根误差（RMSE）为0.847的CSO-power模型计算效率更高，预测能力更好， 分别。此外，敏感性分析揭示了与其他输入参数相比，词干提取参数对 D80 的影响。