MaOEA: Many Objective Evolutionary Algorithm (original) (raw)
A set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: 'SMS-EMOA' <doi:10.1016/j.ejor.2006.08.008> 'NSGA-III' <doi:10.1109/TEVC.2013.2281535> 'MO-CMA-ES' <doi:10.1145/1830483.1830573> The following many-objective benchmark problems are also provided: 'DTLZ1'-'DTLZ4' from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and 'WFG4'-'WFG9' from Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.
Version: | 0.6.2 |
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Imports: | reticulate, nsga2R, lhs, nnet, stringr, randtoolbox, e1071, MASS, gtools, stats, utils, pracma |
Suggests: | testthat |
Published: | 2020-08-31 |
DOI: | 10.32614/CRAN.package.MaOEA |
Author: | Dani Irawan [aut, cre] |
Maintainer: | Dani Irawan <irawan_dani at yahoo.com> |
BugReports: | https://github.com/dots26/MaOEA/issues |
License: | GPL (≥ 3) |
URL: | https://github.com/dots26/MaOEA |
NeedsCompilation: | no |
SystemRequirements: | Python 3.x with following modules: PyGMO, NumPy, and cloudpickle |
Citation: | MaOEA citation info |
Materials: | README |
In views: | Optimization |
CRAN checks: | MaOEA results |
Documentation:
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