Metaheuristic and Gradient-Based Optimization for Neural Network Training and Continuous Problems R package metANN version 0.1.0 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>. Software Burak Dilber <burakdilber91@gmail.com> Comprehensive R Archive Network (CRAN) Burak Dilber [aut, cre, cph], A. Fırat Özdemir [aut, ths] MIT + file LICENSE (https://CRAN.R-project.org/package=metANN/LICENSE) 2026-05-15 application/tgz https://CRAN.R-project.org/package=metANN doi:10.32614/CRAN.package.metANN