- Title
- COSMA-RF : new intelligent model based on chaos optimized slime mould algorithm and random forest for estimating the peak cutting force of conical picks
- Creator
- Zhou, Jian; Dai, Yong; Du, Kun; Khandelwal, Manoj; Li, Chuanqi; Qiu, Yingui
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189514
- Identifier
- vital:17471
- Identifier
-
https://doi.org/10.1016/j.trgeo.2022.100806
- Identifier
- ISSN:2214-3912 (ISSN)
- Abstract
- 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
- Publisher
- Elsevier Ltd
- Relation
- Transportation Geotechnics Vol. 36, no. (2022), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022 Published by Elsevier Ltd
- Subject
- 4005 Civil engineering Chaos optimized slime mould algorithm; Conical picks; Cutting force; Random forest
- Reviewed
- Funder
- This research was funded by the National Science Foundation of China ( 42177164 ), the Innovation-Driven Project of Central South University (No. 2020CX040 ), the Fundamental Research Funds for the Central Universities of Central South University (2022ZZTS0480) and Postgraduate Innovative Project of Central South University (1053320213104).
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