https://github.com/ovidner/openmdao-nsga

NSGA-II and NSGA-III implementations for OpenMDAO
https://github.com/ovidner/openmdao-nsga

Last synced: 16 days ago
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NSGA-II and NSGA-III implementations for OpenMDAO

• Host: GitHub
• URL: https://github.com/ovidner/openmdao-nsga
• Owner: ovidner
• License: mit
• Created: 2020-11-09T08:53:57.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2023-01-12T14:57:34.000Z (about 2 years ago)
• Last Synced: 2024-12-15T08:42:07.269Z (about 2 months ago)
• Language: Python
• Size: 67.4 KB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: CITATION.cff

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README

        
# OpenMDAO-NSGA

[![DOI](https://zenodo.org/badge/DOI/10/f762.svg)](https://doi.org/f762)

NSGA-II and NSGA-III implementations for OpenMDAO.

Some notable features include:

* Support for continuous, integer, discrete and categorical variables.

* Support for Pareto-optimization of multi-objective problems.

* Constraint handling without parameters (using the method described in the original NSGA-II paper).

* Flexible termination criteria.

Do note that the performance for discrete problems will probably be bad, but if you are looking for a fairly robust *shotgun approach* for a variety of different problems, OpenMDAO-NSGA might serve you well enough.

## Installation

This assumes you are using Conda. Run this in your environment to install:

    conda install ovidner::openmdao-nsga

## Usage example

```python

import openmdao.api as om

import omnsga

prob = om.Problem()

...

# Real or integer design var

prob.model.add_design_var("x_1", lower=0, upper=1)

# Discrete design var

omnsga.add_design_var(prob.model, "x_2", values={0, 1, 4}, type=omnsga.VariableType.ORDINAL)

# Categorical design var

omnsga.add_design_var(prob.model, "x_3", values={True, False, None}, type=omnsga.VariableType.NOMINAL)

prob.model.add_constraint("g", lower=0, upper=1)

# Maximize f_1

prob.model.add_objective("f_1", scaler=-1)

# Minimize f_2

prob.model.add_objective("f_2")

prob.driver = omnsga.Nsga2Driver(

    termination_criterion=omnsga.MaxEvaluationsCriterion(500),

)

prob.run_driver()

```