DEoptimR

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).

Last 30 days

This package has been downloaded 92,390 times in the last 30 days. The kind of number that gets mentioned in a keynote speech. Well done! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2,510 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 848,550 times in the last 365 days. Half a million downloads! This work is now a household name in certain academic circles. The day with the most downloads was Dec 17, 2024 with 7,960 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.