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metaSVR  

Support Vector Regression with Metaheuristic Algorithms Optimization
View on CRAN: Click here


Download and install metaSVR package within the R console
Install from CRAN:
install.packages("metaSVR")

Install from Github:
library("remotes")
install_github("cran/metaSVR")

Install by package version:
library("remotes")
install_version("metaSVR", "0.1.0")



Attach the package and use:
library("metaSVR")
Maintained by
Rechtiana Putri Arini
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-08-19
Latest Update: 2025-08-19
Description:
Provides a hybrid modeling framework combining Support Vector Regression (SVR) with metaheuristic optimization algorithms, including the Archimedes Optimization Algorithm (AO) (Hashim et al. (2021) <doi:10.1007/s10489-020-01893-z>), Coot Bird Optimization (CBO) (Naruei & Keynia (2021) <doi:10.1016/j.eswa.2021.115352>), and their hybrid (AOCBO), as well as several others such as Harris Hawks Optimization (HHO) (Heidari et al. (2019) <doi:10.1016/j.future.2019.02.028>), Gray Wolf Optimizer (GWO) (Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>), Ant Lion Optimization (ALO) (Mirjalili (2015) <doi:10.1016/j.advengsoft.2015.01.010>), and Enhanced Harris Hawk Optimization with Coot Bird Optimization (EHHOCBO) (Cui et al. (2023) <doi:10.32604/cmes.2023.026019>). The package enables automatic tuning of SVR hyperparameters (cost, gamma, and epsilon) to enhance prediction performance. Suitable for regression tasks in domains such as renewable energy forecasting and hourly data prediction. For more details about implementation and parameter bounds see: Setiawan et al. (2021) <doi:10.1016/j.procs.2020.12.003> and Liu et al. (2018) <doi:10.1155/2018/6076475>.
How to cite:
Rechtiana Putri Arini (2025). metaSVR: Support Vector Regression with Metaheuristic Algorithms Optimization. R package version 0.1.0, https://cran.r-project.org/web/packages/metaSVR. Accessed 24 Oct. 2026.
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