metANN: Metaheuristic and Gradient-Based Optimization for Neural Network Training and Continuous Problems

Provides tools for general-purpose continuous optimization and feed-forward artificial neural network training using metaheuristic and gradient-based optimization algorithms. The package supports benchmark function optimization, regression, binary classification, and multi-class classification with multilayer perceptrons. The package implements several optimization methods, including particle swarm optimization Kennedy and Eberhart (1995) <doi:10.1109/ICNN.1995.488968>, differential evolution Storn and Price (1997) <doi:10.1023/A:1008202821328>, grey wolf optimizer Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>, secretary bird optimization Fu et al. (2024) <doi:10.1007/s10462-024-10729-y>, and Adam Kingma and Ba (2015) <doi:10.48550/arXiv.1412.6980>.

Version:	0.1.0
Published:	2026-05-15
DOI:	10.32614/CRAN.package.metANN
Author:	Burak Dilber [aut, cre, cph], A. Fırat Özdemir [aut, ths]
Maintainer:	Burak Dilber <burakdilber91 at gmail.com>
BugReports:	https://github.com/burakdilber/metANN/issues
License:	MIT + file LICENSE
URL:	https://github.com/burakdilber/metANN
NeedsCompilation:	no
Citation:	metANN citation info
Materials:	README
CRAN checks:	metANN results

Documentation:

Reference manual:

metANN.html , metANN.pdf

Downloads:

Package source:	metANN_0.1.0.tar.gz
Windows binaries:	r-devel: metANN_0.1.0.zip, r-release: metANN_0.1.0.zip, r-oldrel: metANN_0.1.0.zip
macOS binaries:	r-release (arm64): metANN_0.1.0.tgz, r-oldrel (arm64): metANN_0.1.0.tgz, r-release (x86_64): metANN_0.1.0.tgz, r-oldrel (x86_64): metANN_0.1.0.tgz

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