Pymoo

Minor Release mostly fixing bugs and issues that were reported.

- Breaking changes: Factory methods have been deprecated or deactivated (because of maintenance overhead and hiding of constructor parameters)
- New Problems: DF (for dynamic optimization)
- New Algorithms: G3-PCX, SMS-EMOA, RVEA, AGE-MOEA2, DNSGA2
- Mixed Variable Optimization: Improved support for mixed variable optimization.
- Hyperparameter Tuning: Basic interface for hyperparameter tuning
- Constrained Handling: Improved tutorial explaining how constrained can be handled within different kinds of algorithms
- New Termination Criteria: The implementation now requires returning a floating point number. It is initialized by zero, and a one indicates the algorithm has terminated. This also allows activating a progress bar.
- New Parallelization: The interface has been changed and a class for running a parallel evaluation has been defined.

- New Theme: pymoo got a new HTML theme, responsive, and has a better navigation bar.
- New Project Structure: This includes some breaking changes. Now, the algorithms are grouped into different categories. For instance, NSGA2 is now available at pymoo.algorithms.moo.NSGA2.
- New Algorithms: RVEA, AGEMOEA, ES, SRES, ISRES
- New Problem Implementation: The new version distinguishes between a Problem and an ElementwiseProblem. This has the advantage of handling the two different implementations on an object level.
- New Interface: Most algorithms follow the infill and advance schema, which makes it very simple to write a for loop-based approach and customizing the algorithm’s default behavior (for instance, a local search)
- New Getting Started Guide consisting of five parts explaining better how pymoo can be used. The different alternatives of defining a problem and running an algorithm have been outsourced to the corresponding tutorial pages.

- Improved Getting Started Guide with a new interface of providing functions instead of implementing the problem class
- New Algorithm: PSO for single-objective problems
- New Loop-wise Execution: The algorithm object can be used directly by calling its next method
- New Tutorial: An implementation of checkpoints to resume runs
- New Test Problems Suites (Constrained): DAS-CMOP and MW (contributed by cyrilpic)
- New Operators for Permutations: OrderCrossover and InversionMutation and usage to optimize routes for the TSP and Flowshop problem (contributed by Peng-YM )
- New Crossover: Parent Centric Crossover (PCX) which is known to work well on problems where some variables have dependencies on each other
- Bugfix: Remove evaluation calls in Problem class during print

- New Feature: Riesz s-Energy Method to generate a well-spaced point-set on the unit simplex (reference directions) of arbitrary size.
- New Algorithm: An implementation of Hooke and Jeeves Pattern Search (well-known single-objective algorithm)
- New Documentation: We have re-arranged the documentation and explain now the minimize interface in more detail.
- New Feature: The problem can be parallelized by directly providing a starmapping callable (Contribution by Josh Karpel).
- Bugfix: MultiLayerReferenceDirectionFactory did not work because the scaling was disabled.

- New Algorithm: CMA-ES (Implementation published by the Author)
- New Algorithm: Biased-Random Key Genetic Algorithm (BRKGA)
- New Test Problems: WFG
- New Termination Criterion: Stop an Algorithm based on Time
- New Termination Criterion: Objective Space Tolerance for Multi-objective Problems
- New Display: Easily modify the Printout in each Generation
- New Callback: Based on a class now to allow to store data in the object.
- New Visualization: Videos can be recorded to follow the algorithm's progress.
- Bugfix: NDScatter Plot
- Bugfix: Hypervolume Calculations (Vendor Library)