July 11, 2022: It just happened. The new pymoo (version 0.6.0) version has been released. Many things happened under the hood; however, the code base has changed quite a bit. The individual class has been reimplemented, and the meta algorithms can now be constructed much simpler. New algorithms have been added (G3PXC, RVEA, SMS-EMOA), and dynamic optimization problems and a simple implementation of D-NSGA-II are available. For more details, please have a look at the changelogs. (Release Notes)

September 12, 2021: After quite some time, a bigger release of pymoo (version 0.5.0) is available. The project has made significant progress regarding its structure and has an entirely new module organization. Even though there might be some breaking changes for users, it shall improve the clarity and readability of code in the long term. The documentation has gotten a completely new design and become responsive. In addition, some more algorithms have been improved (PSO, DE) and added (AGEMOEA, ES, SRES, ISRES). For more details, please have a look at the changelogs. (Release Notes)

September 4, 2020: We are more than happy to announce that a new version of pymoo (version 0.4.2) is available. This version has some new features and evolutionary operators, as well as an improved getting, started guide. For more details, please have a look at the release notes. (Release Notes)

May 4, 2020: A new release of pymoo is available. Version 0.4.1 of our framework contains a novel method to generate an arbitrary number of reference directions. Reference directions are required to run most of the many-objective optimization algorithms such as NSGA3 or MOEAD. Moreover, we have fixed minor bugs and provide a basic implementation of the well-known Hooke and Jeeves Pattern Search algorithm for single-objective problems. (Release Notes)

April 3, 2020: We are glad to announce pymoo 0.4.0 has been released. We have added two new algorithms (BRKGA, CMAES) and added the test problem suite WFG. Additionally, we have improved the Display Module to create custom printouts and developed a new termination criterion for multi-objective algorithms. (Release Notes)

October 21, 2019: We have released pymoo 0.3.2. We have added one new single-objective optimization algorithm, a performance metric called KKTPM, and some new tutorials. (Release Notes)

August 16, 2019: We are glad to announce pymoo 0.3.1 has been released. Many new features were added, for instance, visualization and decision making. (Release Notes)

April 10, 2019: The framework has reached a new degree of professionality by improving the software documentation regarding tutorial and API.