GWO: Grey Wolf Optimizer

In metaSVR: Support Vector Regression with Metaheuristic Algorithms Optimization

Grey Wolf Optimizer

Description

An algorithm built by Mirjalili et al. (2014) inspired by leadership hierarchy and hunting mechanism of grey wolves in nature to optimized real-valued objective function in continuous search space in a population-based manner.

Usage

GWO(N, Max_iter, lb, ub, dim, fobj)

Arguments

N	An integer indicate population size.
Max_iter	An integer indicate maximum number of iterations.
lb	A numeric vector that show lower bounds of the search space. One value per dimension.
ub	A numeric vector that show upper bounds of the search space. One value per dimension.
dim	An integer show the number of dimension (parameters) of the problem to optimize. It indicate the number of parameters to be optimized.
fobj	An objective function used to be minimized. It is return single numeric value that show evaluation matrix result in every iteration. It used to calculate the best fitness in every iteration.

Details

This algorithm proposed social hierarchy on GWO to obtain the best fitness and get the best proposed hunting method to locate probable position of the pray. Adaptive values on alpha and A make it possible smooth transition between exploration and exploitation phase.

The algorithm performs until maximum iteration reached or convergence condition when the difference in objective values for ten consecutive times is less than 10^-5.

Value

A list containing:

best_fitness

The best (minimum) fitness value found.

best_position

The parameter vector (position) corresponding to the best fitness.

jml_iter

The number of iterations executed.

param

Matrix of best parameters found across every iterations (dim × iter).

param_list

Vector of best fitness values at each iteration.

Note

The input vectors 'lb' and 'ub' must have the same length as the number of dimensions 'dim'.

This optimization function used inside svrHybrid function.

References

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007

Examples

{
sphere_fn <- function(x) sum(x^2) # simple function for objective function

# GWO optimization
set.seed(123)
result <- GWO(N = 20, Max_iter = 50, lb = c(-5,-5,-5), ub = c(5,5,5), dim = 3, fobj = sphere_fn)

# View best fitness and position found
result$best_fitness
result$best_position
}

metaSVR documentation built on Aug. 21, 2025, 5:58 p.m.