private ParticleSwarmOptimization CreatePsoRastriginSample()
{
ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
#region Problem Configuration
var problem = new SingleObjectiveTestFunctionProblem();
var provider = new SOTFInstanceProvider();
problem.Load(provider.LoadData(provider.GetDataDescriptors().Single(x => x.Name == "Rastrigin Function")));
problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
#endregion
#region Algorithm Configuration
pso.Name = "Particle Swarm Optimization - Rastrigin";
pso.Description = "A particle swarm optimization algorithm which solves the 2-dimensional Rastrigin test function.";
pso.Problem = problem;
pso.Inertia.Value = 0.721;
pso.MaxIterations.Value = 200;
pso.NeighborBestAttraction.Value = 1.193;
pso.PersonalBestAttraction.Value = 1.193;
pso.SwarmSize.Value = 40;
pso.TopologyInitializer = pso.TopologyInitializerParameter.ValidValues.OfType <SPSORandomTopologyInitializer>().First();
pso.TopologyUpdater = pso.TopologyUpdaterParameter.ValidValues.OfType <SPSOAdaptiveRandomTopologyUpdater>().First();
pso.Seed.Value = 0;
pso.SetSeedRandomly.Value = true;
#endregion
pso.Engine = new ParallelEngine.ParallelEngine();
return(pso);
}
public void ShouldRun()
{
var pso = new ParticleSwarmOptimization(2, 10, 10, -10, (double[] d) => { return(Math.Abs((d[0] + d[1]) - 5)); }, (double f) => { return(f == 0); }, FitnessMethod.Minimize);
pso.RunUntilTargetReached();
Assert.True(pso.BestSwarmFitness == 0);
}
private ParticleSwarmOptimization CreatePsoSchwefelSample()
{
ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
#region Problem Configuration
var problem = new SingleObjectiveTestFunctionProblem();
problem.BestKnownQuality.Value = 0.0;
problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 420.968746, 420.968746 });
problem.Bounds = new DoubleMatrix(new double[, ] {
{ -500, 500 }
});
problem.EvaluatorParameter.Value = new SchwefelEvaluator();
problem.Maximization.Value = false;
problem.ProblemSize.Value = 2;
problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
#endregion
#region Algorithm Configuration
pso.Name = "Particle Swarm Optimization - Schwefel";
pso.Description = "A particle swarm optimization algorithm which solves the 2-dimensional Schwefel test function (based on the description in Pedersen, M.E.H. (2010). PhD thesis. University of Southampton)";
pso.Problem = problem;
pso.Inertia.Value = 10;
pso.MaxIterations.Value = 1000;
pso.NeighborBestAttraction.Value = 0.5;
pso.PersonalBestAttraction.Value = -0.01;
pso.SwarmSize.Value = 50;
var inertiaUpdater = pso.InertiaUpdaterParameter.ValidValues
.OfType <ExponentialDiscreteDoubleValueModifier>()
.Single();
inertiaUpdater.StartValueParameter.Value = new DoubleValue(10);
inertiaUpdater.EndValueParameter.Value = new DoubleValue(1);
pso.InertiaUpdater = inertiaUpdater;
pso.ParticleCreator = pso.ParticleCreatorParameter.ValidValues
.OfType <RealVectorParticleCreator>()
.Single();
var swarmUpdater = pso.SwarmUpdaterParameter.ValidValues
.OfType <RealVectorSwarmUpdater>()
.Single();
swarmUpdater.VelocityBoundsIndexParameter.ActualName = "Iterations";
swarmUpdater.VelocityBoundsParameter.Value = new DoubleMatrix(new double[, ] {
{ -10, 10 }
});
swarmUpdater.VelocityBoundsStartValueParameter.Value = new DoubleValue(10.0);
swarmUpdater.VelocityBoundsEndValueParameter.Value = new DoubleValue(1.0);
swarmUpdater.VelocityBoundsScalingOperatorParameter.Value = swarmUpdater.VelocityBoundsScalingOperatorParameter.ValidValues
.OfType <ExponentialDiscreteDoubleValueModifier>()
.Single();
pso.TopologyInitializer = null;
pso.TopologyUpdater = null;
pso.SwarmUpdater = swarmUpdater;
pso.Seed.Value = 0;
pso.SetSeedRandomly.Value = true;
#endregion
pso.Engine = new ParallelEngine.ParallelEngine();
return(pso);
}
public void ShouldRun10Times()
{
int i = 0;
var pso = new ParticleSwarmOptimization(2, 10, 50, -50, (double[] d) => { return(Math.Abs((d[0] + d[1]) - 5)); }, (double f) =>
{
i++;
return(i == 10);
}, FitnessMethod.Minimize);
pso.RunUntilTargetReached();
Assert.True(i == 10);
}
public void ShouldMaximizeFunction()
{
int i = 0;
var pso = new ParticleSwarmOptimization(2, 10, 50, -50, (double[] d) => { return(Math.Abs((d[0] + d[1]) - 5)); }, (double f) =>
{
i++;
return(i == 10);
}, FitnessMethod.Maximize);
double startBestFitness = pso.BestSwarmFitness;
pso.RunUntilTargetReached();
Assert.True(pso.BestSwarmFitness > startBestFitness);
}
