COSMA-RF: New intelligent model based on chaos optimized slime mould algorithm and random forest for estimating the peak cutting force of conical picks

算法 前角 人工神经网络 随机森林 数学 计算机科学 人工智能 工程类 机械工程 机械加工
作者
Jian Zhou,Yong Dai,Kun Du,Manoj Khandelwal,Chuanqi Li,Yingui Qiu
出处
期刊:Transportation geotechnics [Elsevier BV]
卷期号:36: 100806-100806 被引量:39
标识
DOI:10.1016/j.trgeo.2022.100806
摘要

Since conical pick cutting is a complex process of multi-factor coupling effects, theoretical model construction for cutting force prediction is a quite difficult task. In this paper, various novel intelligent models based on chaos-optimized slime mould algorithm (COSMA) and random forest (RF) are proposed for this task. In the proposed COSMA-RF methods, the chaos algorithms with the ergodicity and randomness are introduced to chaotically determine the initial position to form a COSMA, and the SMA and COSMA are used to tune the hyperparameters of RF and mean square error are assigned as a fitness function. Consequently, 205 data samples having seven variables (tensile strength of the rock σ t , compressive strength of the rock σ c , cone angle θ , cutting depth d , attack angle γ , rake angle α and back-clearance angle β ) and one output parameter peak cutting force ( PCF ) are collected from previous literature. Additionally, the performance of optimal COSMA-RF models is comprehensively compared with the existing theoretical formulae and four common machine algorithms, namely RF, extreme gradient boosting, extreme learning machine and back propagation neural network. The results indicate that Logistic map optimized SMA (LSMA), Sine map optimized SMA (SINSMA) and Sinusoidal map optimized SMA (SSMA) have better convergence ability and accuracy compared with original SMA. LSMA-RF, SINSMA-RF and SSMA-RF models yield better PCF prediction performance compared with theoretical formulae and common machine algorithms. Furthermore, sensitive analysis shows σ t , σ c , d and β are significantly sensitive to PCF .
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