sboa: Secretary Bird Optimization Algorithm

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

Secretary Bird Optimization Algorithm

Description

General-purpose continuous optimization using the Secretary Bird Optimization Algorithm (SBOA).

Usage

sboa(
  fn,
  lower,
  upper,
  n_agents = 30,
  max_iter = 500,
  ...,
  verbose = TRUE,
  seed = NULL
)

Arguments

fn	Objective function to be minimized, or a character string naming a built-in benchmark function such as "F1".
lower	Lower bounds for decision variables.
upper	Upper bounds for decision variables.
n_agents	Number of search agents.
max_iter	Maximum number of iterations.
...	Additional arguments passed to fn.
verbose	Logical; if TRUE, progress is printed.
seed	Optional random seed.

Value

An object of class "sboa".

References

Fu, W., Wang, K., Liu, J., et al. (2024). Secretary Bird Optimization Algorithm. Artificial Intelligence Review. DOI: 10.1007/s10462-024-10729-y

Examples

sphere <- function(x) sum(x^2)

res <- sboa(
  fn = sphere,
  lower = rep(-10, 5),
  upper = rep(10, 5),
  n_agents = 10,
  max_iter = 20,
  seed = 123,
  verbose = FALSE
)

res2 <- sboa(
  fn = "F1",
  lower = rep(-100, 5),
  upper = rep(100, 5),
  n_agents = 10,
  max_iter = 20,
  seed = 123,
  verbose = FALSE
)

print(res)
print(res2)
list_benchmarks()
get_benchmark("F9")

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