cs-moea 1.0.2

dotnet add package cs-moea --version 1.0.2
NuGet\Install-Package cs-moea -Version 1.0.2
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="cs-moea" Version="1.0.2" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add cs-moea --version 1.0.2
#r "nuget: cs-moea, 1.0.2"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install cs-moea as a Cake Addin
#addin nuget:?package=cs-moea&version=1.0.2

// Install cs-moea as a Cake Tool
#tool nuget:?package=cs-moea&version=1.0.2

cs-moea

Multi-Objective Evolutionary Algorithms implemented in .NET

Features

The following MOEAs are supported:

  • MOEAD
  • NSGA-II
  • GDE-3
  • HAP-MOEA
  • Hybrid-Game

The library supports both multi-objective and multi-constraints optimization problem in which the solutions are continuous vectors.

Usage

Please refer to the sample codes in the cs-moea-samples project for how to use the library to solve various optimization problems.

The cs-moea-samples-gui-winforms project shows the demo of the multi-objective optimization using these algorithm with a GUI that shows the pareto front of the MOEA results. A number of benchmarks are included for comparing various MOEA implementations:

  • NDND
  • NGPD
  • TNK
  • OKA2
  • SYMPART

The details these implementations can be found in MOEA.Benchmarks namespace of the cs-moea project.

The section below provides some details on how to do this using various MOEAs.

NSGA-II to solve NDND

The following sample codes show how to use NSGA-II to solve the NDND multi-objective optimization problem:

NSGAII<ContinuousVector> algorithm = new NSGAII<ContinuousVector>(new NDNDProblem());

algorithm.PopulationSize = 100;

algorithm.Initialize();

while (!algorithm.IsTerminated)
{
	algorithm.Evolve();
	Console.WriteLine("Current Generation: {0}", algorithm.CurrentGeneration);
	Console.WriteLine("Size of Archive: {0}", algorithm.NondominatedArchiveSize);
}

ContinuousVector finalSolution = algorithm.GlobalBestSolution;
NondominatedPopulation<ContinuousVector> paretoFront = algorithm.NondominatedArchive;

Where the NDNDProblem class is defined as below:

public class NDNDProblem: IMOOProblem
{
	public int GetObjectiveCount()
	{
		return 2;
	}

	public int GetDimensionCount()
	{
		return 2;
	}

	public bool IsFeasible(MOOSolution s)
	{
		return true;
	}

	public bool IsMaximizing()
	{
		return false;
	}

	public double CalcObjective(MOOSolution s, int objective_index)
	{
		ContinuousVector x = (ContinuousVector)s;

		double f1 = 1 - System.Math.Exp((-4) * x[0]) * System.Math.Pow(System.Math.Sin(5 * System.Math.PI * x[0]), 4);
		if (objective_index == 0)
		{
			return f1;
		}
		else
		{
			double f2, g, h;
			if (x[1] > 0 && x[1] < 0.4)
				g = 4 - 3 * System.Math.Exp(-2500 * (x[1] - 0.2) * (x[1] - 0.2));
			else
				g = 4 - 3 * System.Math.Exp(-25 * (x[1] - 0.7) * (x[1] - 0.7));
			double a = 4;
			if (f1 < g)
				h = 1 - System.Math.Pow(f1 / g, a);
			else
				h = 0;
			f2 = g * h;
			return f2;
		}
	}

	public double GetUpperBound(int dimension_index)
	{
		return 1;
	}

	public double GetLowerBound(int dimension_index)
	{
		return 0;
	}
}

GDE3 to solve NDND

The following sample codes show how to use GDE-3 to solve the NDND multi-objective optimization problem:

 GDE3<ContinuousVector> algorithm = new GDE3<ContinuousVector>(new TNKProblem());

algorithm.PopulationSize = 100;


algorithm.Initialize();

while (!algorithm.IsTerminated)
{
	algorithm.Evolve();
	Console.WriteLine("Current Generation: {0}", algorithm.CurrentGeneration);
	Console.WriteLine("Size of Archive: {0}", algorithm.NondominatedArchiveSize);
}
ContinuousVector finalSolution = algorithm.GlobalBestSolution;
NondominatedPopulation<ContinuousVector> paretoFront = algorithm.NondominatedArchive;

HAP-MOEA

The following sample codes show how to use HAP-MOEA to solve the NDND multi-objective optimization problem:

 HAPMOEA<ContinuousVector> algorithm = new HAPMOEA<ContinuousVector>(new NDNDProblem());

algorithm.Config.PopulationSize = 100;


algorithm.Initialize();

while (!algorithm.IsTerminated)
{
	algorithm.Evolve();
	Console.WriteLine("Current Generation: {0}", algorithm.CurrentGeneration);
	Console.WriteLine("Size of Archive: {0}", algorithm.NondominatedArchiveSize);
}
NondominatedPopulation<ContinuousVector> paretoFront = algorithm.NondominatedArchive;

Hybrid-Game

The following sample codes show how to use Hybrid-Game to solve the NDND multi-objective optimization problem:


HybridGame<ContinuousVector> algorithm = new HybridGame<ContinuousVector>(new NDNDProblem());

algorithm.Config.PopulationSize = 100;


algorithm.Initialize();

while (!algorithm.IsTerminated)
{
	algorithm.Evolve();
	Console.WriteLine("Current Generation: {0}", algorithm.CurrentGeneration);
	Console.WriteLine("Size of Archive: {0}", algorithm.NondominatedArchiveSize);
}
NondominatedPopulation<ContinuousVector> paretoFront = algorithm.NondominatedArchive;
Product Compatible and additional computed target framework versions.
.NET Framework net452 is compatible.  net46 was computed.  net461 was computed.  net462 was computed.  net463 was computed.  net47 was computed.  net471 was computed.  net472 was computed.  net48 was computed.  net481 was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.

This package has no dependencies.

NuGet packages

This package is not used by any NuGet packages.

GitHub repositories (1)

Showing the top 1 popular GitHub repositories that depend on cs-moea:

Repository Stars
ArsenAbazian/CryptoTradingFramework
A self hosted, cryptocurrency trading bot and framework supporting multiple exchanges with GUI
Version Downloads Last updated
1.0.2 2,624 12/4/2017
1.0.1 1,192 11/13/2017

Multi-Objective Evolutionary Algorithms in .NET 4.5.2