pymoo

VERSION 0.6.1.5

• New Build System (contributed by @tpgillam)
• Miscellaneous other improvements
• Fix some typos in documentation

VERSION 0.6.1.3

• Compatibility with Numpy 2.0
• Make Autograd for Automatic Differentiation Optional
• Incorporate all Bug Fixes from Pull Requests

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)

• New Algorithm: Nelder Mead with box constraint handling in the design space
• New Performance indicator: Karush Kuhn Tucker Proximity Measure (KKTPM)
• Added Tutorial: Equality constraint handling through customized repair
• Added Tutorial: Subset selection through GAs
• Added Tutorial: How to use custom variables
• Bugfix: No pf given for problem, no feasible solutions found

NEW Major Release: pymoo 0.3.1

• Merging pymop into pymoo - all test problems are included
• Improved Getting Started Guide
• Added Visualization
• Added Decision Making
• Added GD+ and IGD+
• New Termination Criteria “x_tol” and “f_tol”
• Added Mixed Variable Operators and Tutorial
• Refactored Float to Integer Operators
• Fixed NSGA-III Normalization Variable Swap
• Fixed casting issue with latest NumPy version for integer operators
• Removed the dependency of Cython for installation (.c files are delivered now)

NEW Major Release: pymoo 0.3.0

• New documentation and webpage (https://pymoo.org)
• Improved version of Differential Evolution (jitter, dither)
• New crossovers (UX, HUX, ...)
• R-NSGA-II Implementation added