A new auto-tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm
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ORIGINAL ARTICLE
A new auto‑tuning model for predicting the rock fragmentation: a cat swarm optimization algorithm Jiandong Huang1 · Panagiotis G. Asteris2 · Siavash Manafi Khajeh Pasha3 · Ahmed Salih Mohammed4 · Mahdi Hasanipanah5 Received: 10 September 2020 / Accepted: 26 October 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract 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 D 80 in comparison with other input parameters. Keywords Blasting · Rock fragmentation · Cat swarm optimization · Particle swarm optimization
1 Introduction Economically, blasting is an important part of mining industry, especially in open pit mines, and the main objective of blasting is to reach a proper size of fragmented rock. In fact, properly fragmented rock has positive effects on operations such as loading, hauling, and crushing [1, 2]. This study * Mahdi Hasanipanah [email protected] 1
School of Mines, China University of Mining and Technology, Xuzhou, China
2
Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Heraklion, Athens, Greece
3
IMAGEi Consultant Co., Tokyo, Japan
4
College of Engineering, Civil Engineering Department, University of Sulaimani, Kurdistan Region, Iraq
5
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
aims to tackle the problem related to rock fragmentation. Rock mass has natural properties that are unchangeable, while blast factors have different behaviors. Rock fragmentation is a large-scale problem; thus, effective numerical methods are required to deal with it. Some experimental models have been developed to predict r
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