public static void Run()
{
OptimizationProblem sizingOptimizationProblem = new Truss10Benchmark();
var builder = new DifferentialEvolutionAlgorithmConstrained.Builder(sizingOptimizationProblem);
builder.PopulationSize = 100;
builder.MutationFactor = 0.6;
builder.CrossoverProbability = 0.9;
builder.ConvergenceCriterion = new MaxFunctionEvaluations(200000);
builder.Penalty = new DeathPenalty();
IOptimizationAlgorithm de = builder.Build();
IOptimizationAnalyzer analyzer = new OptimizationAnalyzer(de);
analyzer.Optimize();
// Print results
Console.WriteLine("\n Best Position:");
for (int i = 0; i < sizingOptimizationProblem.Dimension; i++)
{
Console.WriteLine(String.Format(@" x[{0}] = {1} ", i, de.BestPosition[i]));
}
Console.WriteLine(String.Format(@"Best Fitness: {0}", de.BestFitness));
}
public static void Run()
{
int seed = 2;
var rng = new Random(seed);
OptimizationProblem optimizationProblem = new S_CRES();
var builder = new DifferentialEvolutionAlgorithmConstrained.Builder(optimizationProblem);
builder.PopulationSize = 20;
builder.MutationFactor = 0.6;
builder.CrossoverProbability = 0.9;
builder.ConvergenceCriterion = new MaxFunctionEvaluations(100000);
builder.Penalty = new DeathPenalty();
builder.RandomNumberGenerator = rng;
IOptimizationAlgorithm de = builder.Build();
IOptimizationAnalyzer analyzer = new OptimizationAnalyzer(de);
analyzer.Optimize();
//TODO: Not sure this is the exact solution. Needs research.
double expectedFitness = 13.590841691859703;
var expectedDesign = Vector.CreateFromArray(new double[] { 2.246825836986833, 2.3818634605759064 });
Assert.Equal(expectedFitness, de.BestFitness, 6);
Assert.True(Vector.CreateFromArray(de.BestPosition).Equals(expectedDesign, 1E-6));
}
public static void Run()
{
int seed = 1;
var rng = new Random(seed);
OptimizationProblem optimizationProblem = new Rosenbrock();
var builder = new DifferentialEvolutionAlgorithm.Builder(optimizationProblem);
builder.PopulationSize = 100;
builder.MutationFactor = 0.6;
builder.CrossoverProbability = 0.9;
builder.ConvergenceCriterion = new MaxFunctionEvaluations(100000);
builder.RandomNumberGenerator = rng;
IOptimizationAlgorithm de = builder.Build();
IOptimizationAnalyzer analyzer = new OptimizationAnalyzer(de);
analyzer.Optimize();
double expectedFitness = 0.0;
var expectedDesign = Vector.CreateWithValue(optimizationProblem.Dimension, 1.0);
Assert.Equal(expectedFitness, de.BestFitness, 10);
Assert.True(Vector.CreateFromArray(de.BestPosition).Equals(expectedDesign, 1E-6));
}
public static void Run()
{
int seed = 1;
var rng = new Random(seed);
OptimizationProblem optimizationProblem = new Ackley(2);
var builder = new ParticleSwarmOptimizationAlgorithm.Builder(optimizationProblem);
builder.SwarmSize = 10;
builder.PhiP = 2.0;
builder.PhiG = 2.0;
builder.Omega = 0.2;
builder.ConvergenceCriterion = new MaxFunctionEvaluations(10000);
builder.Logger = new NoLogger();
builder.RandomNumberGenerator = rng;
IOptimizationAlgorithm pso = builder.Build();
IOptimizationAnalyzer analyzer = new OptimizationAnalyzer(pso);
analyzer.Optimize();
double expectedFitness = 0.0;
var expectedDesign = Vector.CreateZero(optimizationProblem.Dimension);
Assert.Equal(expectedFitness, pso.BestFitness, 3);
Assert.True(Vector.CreateFromArray(pso.BestPosition).Equals(expectedDesign, 1E-4));
}
