EHHOCBO: Enhanced Harris Hawks Optimization with Coot Bird...

In metaSVR: Support Vector Regression with Metaheuristic Algorithms Optimization

Enhanced Harris Hawks Optimization with Coot Bird Optimization

Description

This function implements a hybrid metaheuristic optimization algorithm that combines Harris Hawks Optimization with leader selection of Coot Bird Optimization to optimized real-valued objective function in continuous search space in a population-based manner built by Cui et al. (2023).

Usage

EHHOCBO(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 start by adding leadership mechanism of CBO into HHO process so it can make better foundation for the global search. Ensemble Mutation Strategy (EMS) to improve the exploration trend and population diversity also Refracted Opposition-Based Learning (ROBL) to update current optimal solution in the swarm added to enhanced the combination of HHO and CBO.

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

Cui, H., Guo, Y., Xiao, Y., Wang, Y., Li, J., Zhang, Y., & Zhang, H. (2023). Enhanced Harris Hawks Optimization Integrated with Coot Bird Optimization for Solving Continuous Numerical Optimization Problems. CMES - Computer Modeling in Engineering and Sciences, 137(2), 1635–1675. https://doi.org/10.32604/cmes.2023.026019

Examples

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

# EHHOCBO optimization
set.seed(123)
result <- EHHOCBO(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.