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Releases: anyoptimization/pymoo

VERSION 0.6.1.3

31 Jul 04:57
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VERSION 0.6.1.3

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

VERSION 0.6.0.1

31 Jan 06:10
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Minor Release mostly fixing bugs and issues that were reported.

VERSION 0.6.0

31 Jul 20:53
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  • 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.

0.5.0

14 Sep 20:02
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  • 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.

0.4.2

04 Sep 18:09
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  • 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

0.4.1

04 May 22:20
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  • 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.

0.4.0

03 Apr 15:27
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  • 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)

0.3.2

21 Oct 16:12
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  • 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

0.3.1

15 Aug 21:30
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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)

0.3.0

26 Apr 20:38
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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