Support Vector Regression with Metaheuristic Algorithms Optimization
Ant Lion Optimizer
Archimedes Optimization
Combined Archimedes Optimization with Coot Bird Optimization
Coot Bird Optimization
Denormalize
Enhanced Harris Hawks Optimization with Coot Bird Optimization
Default Bounds Initialization for SVR Optimization
Grey Wolf Optimizer
Harris Hawks Optimization
Initialize Position on Ant Lion Optimizer
Initialize Position on Enhanced Harris Hawks Optimization with Coot Bi...
Initialize Position on Grey Wolf Optimizer
Initialize Position on Harris Hawks Optimization
Levy Flight Generator
Levy Flight Generator
Calculate Loss Based on Selected Objective Function
Mean Absolute Error
Mean Absolute Percentage Error
Normalize
Perform Random Walk Around Antlion
Root Mean Squared Error
Roulette Wheel Selection
Symmetric Mean Absolute Percentage Error
SVR with Metaheuristic Algorithms Optimization
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>.
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