Opossum—OPtimizatiOn Solver with SUrrogate Models
(by Thomas Wortmann)
Opossum offers some of the best performing optimization algorithms in Grasshopper: model-based RBFOpt and RBFMOpt and evolutionary CMA-ES and NSGA-II.
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Opossum includes two of the best-performing,single-objective optimization algorithms in Grasshopper: model-based RBFOpt and evolutionary CMA-ES. It also includes the multi-objective RBFMOpt, and the multi-objective MACO (Ant Colony), MOEA/D, NSGA-II and NSPSO (Particle Swarm) algorithms from the Pygmo 2 library.

RBFOpt uses advanced machine learning techniques to find good solutions with a small number of function evaluations, i.e. simulations, while CMA-ES reliably finds near-optimal solutions when many function evaluations are possible. (Various papers with benchmark results are availabe here.) RBFMOpt won the “2-Objective Expensive” track of the international Black Box Optimization Competition 2019, surpassing the winner of the previous two years, the commercial algorithm Artelys Nitro.

Opossum's GUI is similar to Galapagos. Opossum has a results table, which makes it easy to revisit all optimization results by double-clicking entries in the table.

Opossum requires a free license key, please email support to get one.

Opossum 1.5.0 and lower has been developed at the Advanced Architecture Lab with support from the SUTD-MIT International Design Centre.

Version History

Opossum 2.2.4       2021-April-07

  • Opossum does not expire the whole canvas anymore
  • "Tuned" default parameters for RBFMOpt

Opossum 2.2.3       2020-December-09

  • Fixed a bug with serialization in Grasshopper

Opossum 2.2.2       2020-December-01

  • Results table is more responsive
  • Results table is sortable
  • Fixed a bug where clicking the results table did not reset to the correct solution

Opossum 2.2.0       2020-October-15

  • Faster interaction with Python back-end
  • Added Paretorank and improved Hypervolume calculation for MOO results
  • Added tooltips for Expert settings
  • Added "augmented Tchebycheff sum", "epsilon", "Do Init?" options for RBFMOpt
  • Close now resets Opossum
  • Update to RBFOpt 4.2.1
  • Fixed Rhino 7 WIP display issue
  • Fixed crash when changing variables

Opossum 2.1.0        2020-May-23

  • Added multi-objective algorithms: MACO, MOEA/D and NSPSO.
  • Expert settings for all algorithms
  • Bug fixes

Opossum 2.0.0        2019-September-09

  • Multi-objective optimization with RBFMOpt and NSGA-II.

Opossum 1.7.1        2019-May-27

  • Genepools for CMAES starting point.

Opossum 1.7.0        2019-April-28

  • Includes CMA-ES algorithm
  • Eliminates need for installer (just drop Opossum.gha and the "Solvers" folder into Libraries)
  • Updated to RBFOpt 4.1.1: New options, more efficient initialization phase

Opossum 1.5.0        2018-March-16

  • Thread-safe
  • Canvas lockscreen
  • Automatic "max_evaluation" setting

Opossum 1.4.3        2017-Sept-30

  • Ensured Invariant Culture

Opossum 1.4.2        2017-Sep-23

  • Bug Fix

Opossum 1.4.1        2017-Sep-17

  • Update to RBFOpt 3.0.1: faster performance with improved local search
  • Results tab allows examination of all optimization results

Opossum 1.2.3        2017-Jun-06

  • Fixed bug caused by very long PATH variable

Opossum 1.2.2        2017-Jun-12

  • Fixed license write-permission issue

Opossum 1.2.1        2017-Jun-12

  • Updated to RBFOpt 2.1: faster performance
  • Included example file
  • Small bugfixes

Opossum 1.1.3        2017-Jun-12

  • Opossum window always on top.

Opossum 1.1.2        2017-Jun-12

  • More compact libraries
  • Culture-independent license check

Opossum 1.1.1        2016-Oct-27

  • GUI Improvements
  • Installer Improvements
  • Bug Fixes
License:
Free
  • Opossum offers some of the best performing optimization algorithms in Grasshopper: model-based RBFOpt and RBFMOpt and evolutionary CMA-ES and NSGA-II.
  • Opossum offers some of the best performing optimization algorithms in Grasshopper: model-based RBFOpt and RBFMOpt and evolutionary CMA-ES and NSGA-II.
  • Opossum offers some of the best performing optimization algorithms in Grasshopper: model-based RBFOpt and RBFMOpt and evolutionary CMA-ES and NSGA-II.
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