cs-optimization-binary-solutions 1.0.1

Numerical Optimization Package in which solutions are binary-coded

Install-Package cs-optimization-binary-solutions -Version 1.0.1
dotnet add package cs-optimization-binary-solutions --version 1.0.1
<PackageReference Include="cs-optimization-binary-solutions" Version="1.0.1" />
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paket add cs-optimization-binary-solutions --version 1.0.1
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#r "nuget: cs-optimization-binary-solutions, 1.0.1"
#r directive can be used in F# Interactive, C# scripting and .NET Interactive. Copy this into the interactive tool or source code of the script to reference the package.
// Install cs-optimization-binary-solutions as a Cake Addin
#addin nuget:?package=cs-optimization-binary-solutions&version=1.0.1

// Install cs-optimization-binary-solutions as a Cake Tool
#tool nuget:?package=cs-optimization-binary-solutions&version=1.0.1
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

cs-optimization-binary-solutions

Local search optimization for binary-coded solutions implemented in C#

Features

The following meta-heuristic algorithms are provided for binary optimization (Optimization in which the solutions are binary-coded):

  • Genetic Algorithm
  • Memetic Algorithm
  • GRASP
  • Multi-start Hill Climbing
  • Tabu Search
  • Variable Neighbhorhood Search
  • Iterated Local Search
  • Random Search

Usage

The code below shows how to use Genetic Algorithm to solve an optimization problem that looks for the binary-coded solution with minimum number of 1 bits:

int popSize = 100;
int dimension = 1000; // solution has 1000 bits
GeneticAlgorithm method = new GeneticAlgorithm(popSize, dimension);
method.MaxIterations = 500;

method.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

BinarySolution finalSolution = method.Minimize((solution, constraints) =>
{
	int num1Bits = 0;
	for(int i=0; i < solution.Length; ++i)
	{
		num1Bits += solution[i];
	}
	return num1Bits; // try to minimize the number of 1 bits in the solution
});
Console.WriteLine("final solution: {0}", finalSolution);

The code below shows how to use Memetic Algorithm to solve an optimization problem that looks for the binary-coded solution with minimum number of 1 bits:

int popSize = 100;
int dimension = 1000; // solution has 1000 bits
MemeticAlgorithm method = new MemeticAlgorithm(popSize, dimension);
method.MaxIterations = 10;
method.MaxLocalSearchIterations = 1000;

method.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

BinarySolution finalSolution = method.Minimize((solution, constraints) =>
{
	int num1Bits = 0;
	for(int i=0; i < solution.Length; ++i)
	{
		num1Bits += solution[i];
	}
	return num1Bits; // try to minimize the number of 1 bits in the solution
});
Console.WriteLine("final solution: {0}", finalSolution);

The code below shows how to use Stochastic Hill Climber to solve an optimization problem that looks for the binary-coded solution with minimum number of 1 bits:

int dimension = 1000; // solution has 1000 bits
StochasticHillClimber method = new StochasticHillClimber(dimension);
method.MaxIterations = 100;

method.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

BinarySolution finalSolution = method.Minimize((solution, constraints) =>
{
	int num1Bits = 0;
	for(int i=0; i < solution.Length; ++i)
	{
		num1Bits += solution[i];
	}
	return num1Bits; // try to minimize the number of 1 bits in the solution
});
Console.WriteLine("final solution: {0}", finalSolution);

The code below shows how to use Iterated Local Search to solve an optimization problem that looks for the binary-coded solution with minimum number of 1 bits:

int dimension = 1000; // solution has 1000 bits
IteratedLocalSearch method = new IteratedLocalSearch(dimension);
method.MaxIterations = 1000;

method.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

BinarySolution finalSolution = method.Minimize((solution, constraints) =>
{
	int num1Bits = 0;
	for(int i=0; i < solution.Length; ++i)
	{
		num1Bits += solution[i];
	}
	return num1Bits; // try to minimize the number of 1 bits in the solution
});
Console.WriteLine("final solution: {0}", finalSolution);

cs-optimization-binary-solutions

Local search optimization for binary-coded solutions implemented in C#

Features

The following meta-heuristic algorithms are provided for binary optimization (Optimization in which the solutions are binary-coded):

  • Genetic Algorithm
  • Memetic Algorithm
  • GRASP
  • Multi-start Hill Climbing
  • Tabu Search
  • Variable Neighbhorhood Search
  • Iterated Local Search
  • Random Search

Usage

The code below shows how to use Genetic Algorithm to solve an optimization problem that looks for the binary-coded solution with minimum number of 1 bits:

int popSize = 100;
int dimension = 1000; // solution has 1000 bits
GeneticAlgorithm method = new GeneticAlgorithm(popSize, dimension);
method.MaxIterations = 500;

method.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

BinarySolution finalSolution = method.Minimize((solution, constraints) =>
{
	int num1Bits = 0;
	for(int i=0; i < solution.Length; ++i)
	{
		num1Bits += solution[i];
	}
	return num1Bits; // try to minimize the number of 1 bits in the solution
});
Console.WriteLine("final solution: {0}", finalSolution);

The code below shows how to use Memetic Algorithm to solve an optimization problem that looks for the binary-coded solution with minimum number of 1 bits:

int popSize = 100;
int dimension = 1000; // solution has 1000 bits
MemeticAlgorithm method = new MemeticAlgorithm(popSize, dimension);
method.MaxIterations = 10;
method.MaxLocalSearchIterations = 1000;

method.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

BinarySolution finalSolution = method.Minimize((solution, constraints) =>
{
	int num1Bits = 0;
	for(int i=0; i < solution.Length; ++i)
	{
		num1Bits += solution[i];
	}
	return num1Bits; // try to minimize the number of 1 bits in the solution
});
Console.WriteLine("final solution: {0}", finalSolution);

The code below shows how to use Stochastic Hill Climber to solve an optimization problem that looks for the binary-coded solution with minimum number of 1 bits:

int dimension = 1000; // solution has 1000 bits
StochasticHillClimber method = new StochasticHillClimber(dimension);
method.MaxIterations = 100;

method.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

BinarySolution finalSolution = method.Minimize((solution, constraints) =>
{
	int num1Bits = 0;
	for(int i=0; i < solution.Length; ++i)
	{
		num1Bits += solution[i];
	}
	return num1Bits; // try to minimize the number of 1 bits in the solution
});
Console.WriteLine("final solution: {0}", finalSolution);

The code below shows how to use Iterated Local Search to solve an optimization problem that looks for the binary-coded solution with minimum number of 1 bits:

int dimension = 1000; // solution has 1000 bits
IteratedLocalSearch method = new IteratedLocalSearch(dimension);
method.MaxIterations = 1000;

method.SolutionUpdated += (best_solution, step) =>
{
	Console.WriteLine("Step {0}: Fitness = {1}", step, best_solution.Cost);
};

BinarySolution finalSolution = method.Minimize((solution, constraints) =>
{
	int num1Bits = 0;
	for(int i=0; i < solution.Length; ++i)
	{
		num1Bits += solution[i];
	}
	return num1Bits; // try to minimize the number of 1 bits in the solution
});
Console.WriteLine("final solution: {0}", finalSolution);

Release Notes

Numerical Optimization Package in which solutions are binary-coded. Based on .NET 4.5.2

Dependencies

This package has no dependencies.

NuGet packages

This package is not used by any NuGet packages.

GitHub repositories

This package is not used by any popular GitHub repositories.

Version History

Version Downloads Last updated
1.0.1 652 11/4/2017