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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>.

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3.00 score

SBOAtools - Secretary Bird Optimization for Continuous Optimization and Neural Network Training

Provides an implementation of Secretary Bird Optimization for general-purpose continuous optimization, benchmark optimization, and training single-hidden-layer feed-forward neural network models. The implemented optimizer is based on the Secretary Bird Optimization Algorithm proposed by Fu et al. (2024) <doi:10.1007/s10462-024-10729-y>. The neural network training functionality is based on Dilber and Özdemir (2026) <doi:10.1007/s00521-026-11874-x>.

Last updated

machine-learningmetaheuristicneural-networkoptimizationsecretary-bird-optimization

3.00 score 1 scripts 455 downloads

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>.

Last updated

1.70 score