cs-optimization-binary-solutions

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

Install

Install-Package cs-optimization-binary-solutions -Version 1.0.1

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);

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