MaOEA: Many Objective Evolutionary Algorithm
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 |
| Imports: |
reticulate, nsga2R, lhs, nnet, stringr, randtoolbox, e1071, MASS, gtools, stats, utils, pracma |
| Suggests: |
testthat |
| Published: |
2020-08-31 |
| 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:
Downloads:
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