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0.6.1 [Documentation]¶

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: As you might have noticed, 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)