sboa_mlp: Train a single-hidden-layer MLP using SBOA

In SBOAtools: Secretary Bird Optimization for Continuous Optimization and Neural Network Training

Train a single-hidden-layer MLP using SBOA

Description

Trains a single-hidden-layer multilayer perceptron with the Secretary Bird Optimization Algorithm.

Usage

sboa_mlp(
  X_train,
  y_train,
  hidden_dim = 10,
  n_agents = 30,
  max_iter = 500,
  lower = -1,
  upper = 1,
  seed = NULL,
  verbose = TRUE
)

Arguments

X_train	Training input data.
y_train	Training output data.
hidden_dim	Number of hidden neurons.
n_agents	Number of search agents.
max_iter	Maximum number of iterations.
lower	Lower bound for parameter search.
upper	Upper bound for parameter search.
seed	Optional random seed.
verbose	Logical; if TRUE, progress is printed.

Value

An object of class "sboa_mlp".

References

Dilber, B., and Ozdemir, A. F. (2026). A novel approach to training feed-forward multi-layer perceptrons with recently proposed secretary bird optimization algorithm. Neural Computing and Applications. DOI: 10.1007/s00521-026-11874-x

Examples

set.seed(123)

X_train <- matrix(runif(40), nrow = 10, ncol = 4)
y_train <- matrix(runif(10), nrow = 10, ncol = 1)

fit <- sboa_mlp(
  X_train = X_train,
  y_train = y_train,
  hidden_dim = 3,
  n_agents = 10,
  max_iter = 20,
  lower = -1,
  upper = 1,
  seed = 123,
  verbose = FALSE
)

print(fit)
pred <- predict(fit, X_train)
head(pred)

SBOAtools documentation built on May 3, 2026, 9:06 a.m.