Releases: esa/pagmo2
pagmo 2.11.4
This point release contains a couple of important bugfixes:
- various sorting/ranking utilities should now be more robust if the input data contains NaNs,
- a critical bug in the generational PSO algorithm was discovered and fixed by @Sceki.
As usual, the full changelog is available here:
pagmo 2.11.3
This new release fixes a few issues involving archipelagos containing multi-objective optimisation problems. It also features improved install docs and various internal fixes/improvements for the Python bindings.
The full changelog, as usual, is available here:
pagmo 2.11.2
Another quick release fixing a couple of issues:
- the MinGW pip builds have been fixed,
- a small inconsistency in the exposition of the NSGA2 algorithm was also corrected.
The full changelog is available at the usual place:
pagmo 2.11.1
This is a quick bugfix release that corrects an issue in the migration framework when multi-objective problems are involved. A few Python docstrings were fixed as well.
As usual, the full changelog is available here:
pagmo 2.11
This is the largest pagmo2 release so far, coming after 7 months of hard work by the development team.
To start off, we have new algorithms contributed by @Sceki and @michiboo, the Extended Ant Colony Optimization (gaco) and the Grey Wolf Optimizer (gwo). @Sceki is our ESA SOCIS (summer of code in space) student and he's working hard on various new algorithmic developments. You'll hear more from him!
Secondly, two large developer-oriented improvements:
- pagmo is now a compiled library, instead of a header-only library. This results in much shorter compilation times for typical testing/development workflows, and it makes the
pagmo.hpp
header usable again; - we switched from Cereal to Boost for the serialization backend. Cereal's development unfortunately seems to have stalled. Additionally, because the library is header-only, it was causing noticeable compilation slowdowns, and it was not playing well with our associated packages API, requiring various hacks that now have been removed.
Then, we have the first version of two new features that have been often requested by the users:
- a batch fitness evaluation framework (bfe), which, among others, allows to parallelise the initialisation of populations, islands and archipelagos. The bfe also allows to accelerate via GPU/SIMD vectorisation the simultaneous computation of the objective function for many decision vectors in batch mode, which can be exploited by population-based algorithms (currently, our implementations of ACO and NSGA2 take advantage of this feature, more algorithms will in the future);
- the migration feature has finally been implemented! This is an improved version of the migration framework which was available in pagmo1, and it allows to implement both topologies and replacement/selection policies directly from Python (whereas in pagmo1 the migration classes could only be coded in C++).
Although these two features are fully functional, there still remains work to be done (e.g., we need to port most of the actual topologies from pagmo1, implement Python-oriented bfes, write proper introductory tutorials/docs, etc.). We expect in the next few weeks to release regularly and often new pagmo versions which will gradually fill in the missing bits.
pagmo 2.12 also features a new problem test suite (WFG) and the usual host of fixes and docs improvements.
As usual, the full changelog is available here:
pagmo 2.10
This release includes a few new features - mostly quality-of-life improvements - and build system fixes.
As usual, the full changelog is available at the following page:
pagmo 2.9
This release features two new main features: the python decorator_problem
problem, which allows to customise at runtime the behaviour of an existing problem (e.g., for adding logging), and the fork_island
island, which enables in C++ the parallelisation of thread-unsafe problems and/or algorithms (such as IPOPT).
There is, as usual, an assortment of smaller new features, bug fixes, and documentation and build system improvements. The full changelog is available, as usual, here:
pagmo 2.8
This release features various bugfixes and a few enhancements. All users are encouraged to upgrade.
The full changelog is available, as usual, here: https://esa.github.io/pagmo2/changelog.html.
pagmo 2.7
This release adds 3 new algorithms:
- particle swarm optimization generational (GPSO),
- exponential natural evolution strategies (xNES),
- improved harmony search (IHS).
The full changelog is available, as usual, here: https://esa.github.io/pagmo2/changelog.html
pagmo 2.6
This release fixes a bug in the NSGA2 code (see #139).
The full changelog is available here: