https://github.com/jbytecode/de

Dart Language implementation of Differential Evolution Optimization
https://github.com/jbytecode/de

Last synced: 2 months ago
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Dart Language implementation of Differential Evolution Optimization

• Host: GitHub
• URL: https://github.com/jbytecode/de
• Owner: jbytecode
• License: mit
• Created: 2020-02-23T17:14:35.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2024-06-21T18:54:01.000Z (11 months ago)
• Last Synced: 2025-01-29T05:34:09.486Z (4 months ago)
• Language: Dart
• Size: 17.6 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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README

        
![Dart CI](https://github.com/jbytecode/de/workflows/Dart%20CI/badge.svg)

A Library for Differential Evolution Optimization

## Usage

A simple usage example:

```dart

import 'package:de/de.dart';

double f(List x) {

  return -(pow(x[0] - 3.14159265, 2.0) + pow(x[1] - exp(1.0), 2.0));

}

void main(){

  DE de = DE(f, [-500, -500], [500, 500], cr: 0.99, F: 1.20, popsize: 300);

  de.iterateN(5000);

  Solution best = de.getBest();

  print(best);

}

```

## Details

* The objective function is always in the form of

```dart

double f(List x){

}

```

* The optimization is always a maximation. If the objective

function of the problem is a minimization, the returned value of the

objective function can be multiplied by -1.0.

* The default parameters of the classical Differential Evolution Optimization

are set to cr = 0.8 and F = 1.0. These optimization parameters can be customized

in the constructor of DE class.