Package: SBOAtools 0.1.1
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>.
Authors:
SBOAtools_0.1.1.tar.gz
SBOAtools_0.1.1.zip(r-4.7-any)SBOAtools_0.1.1.zip(r-4.6-any)SBOAtools_0.1.1.zip(r-4.5-any)
SBOAtools_0.1.1.tgz(r-4.6-any)SBOAtools_0.1.1.tgz(r-4.5-any)
SBOAtools_0.1.1.tar.gz(r-4.7-any)SBOAtools_0.1.1.tar.gz(r-4.6-any)
SBOAtools_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SBOAtools/json (API)
| # Install 'SBOAtools' in R: |
| install.packages('SBOAtools', repos = c('https://burakdilber.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/burakdilber/sboatools/issues
machine-learningmetaheuristicneural-networkoptimizationsecretary-bird-optimization
Last updated from:9ce4adba09. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 106 | ||
| source / vignettes | OK | 264 | ||
| linux-release | OK | 103 | ||
| macos-release | OK | 97 | ||
| macos-oldrel | OK | 125 | ||
| windows-devel | OK | 52 | ||
| windows-release | OK | 55 | ||
| windows-oldrel | OK | 59 | ||
| wasm-release | OK | 91 |
Exports:get_benchmarklist_benchmarkssboasboa_mlp
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Apply min-max normalization using existing bounds | apply_minmax |
| Clip values to lower and upper bounds | clip_bounds |
| Reverse min-max normalization | denormalize_minmax |
| Forward pass for a single-hidden-layer MLP | forward_mlp |
| Get a built-in benchmark definition | get_benchmark |
| Lévy flight step generator | levy_flight |
| List built-in benchmark functions | list_benchmarks |
| Mean absolute error | mae_vec |
| Mean absolute percentage error | mape_vec |
| MSE fitness function for MLP training | mlp_mse_fitness |
| Min-max normalization | normalize_minmax |
| Plot method for SBOA objects | plot.sboa |
| Plot method for SBOA-MLP objects | plot.sboa_mlp |
| Predict method for SBOA-MLP objects | predict.sboa_mlp |
| Print method for SBOA objects | print.sboa |
| Print method for SBOA-MLP objects | print.sboa_mlp |
| Root mean squared error | rmse_vec |
| R-squared | rsq_vec |
| Secretary Bird Optimization Algorithm | sboa |
| Train a single-hidden-layer MLP using SBOA | sboa_mlp |
| Sigmoid activation function | sigmoid |
| Unpack MLP parameter vector | unpack_mlp_params |