DEoptimR: Differential Evolution Optimization in Pure R

Differential Evolution (DE) stochastic heuristic algorithms for global optimization of problems with and without general constraints. The aim is to curate a collection of its variants that (1) do not sacrifice simplicity of design, (2) are essentially tuning-free, and (3) can be efficiently implemented directly in the R language. Currently, it provides implementations of the algorithms 'jDE' by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133> for single-objective optimization and 'NCDE' by Qu et al. (2012) <doi:10.1109/TEVC.2011.2161873> for multimodal optimization (single-objective problems with multiple solutions).

Version: 1.1-3
Imports: stats
Enhances: robustbase
Published: 2023-10-07
DOI: 10.32614/CRAN.package.DEoptimR
Author: Eduardo L. T. Conceicao [aut, cre], Martin Maechler ORCID iD [ctb]
Maintainer: Eduardo L. T. Conceicao <mail at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Materials: NEWS
In views: Optimization
CRAN checks: DEoptimR results


Reference manual: DEoptimR.pdf


Package source: DEoptimR_1.1-3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): DEoptimR_1.1-3.tgz, r-oldrel (arm64): DEoptimR_1.1-3.tgz, r-release (x86_64): DEoptimR_1.1-3.tgz, r-oldrel (x86_64): DEoptimR_1.1-3.tgz
Old sources: DEoptimR archive

Reverse dependencies:

Reverse depends: RobustAFT
Reverse imports: dtangle, OssaNMA, robustbase, ROI.plugin.deoptim
Reverse suggests: ADMUR, cxr, MSCMT


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