/// ------------------------------------------------------
/// SwarmOps - Numeric and heuristic optimization for C#
/// Copyright (C) 2003-2011 Magnus Erik Hvass Pedersen.
/// Please see the file license.txt for license details.
/// SwarmOps on the internet: http://www.Hvass-Labs.org/
/// ------------------------------------------------------
using System.Diagnostics;
namespace SwarmOps.Problems
{
///
/// Griewank benchmark problem.
///
public class Griewank : Benchmark
{
#region Constructors.
///
/// Construct the object.
///
/// Dimensionality of the problem (e.g. 20)
/// Max optimization iterations to perform.
public Griewank(int dimensionality, int maxIterations)
: base(dimensionality, -600, 600, 300, 600, maxIterations)
{
}
#endregion
#region Base-class overrides.
///
/// Name of the optimization problem.
///
public override string Name
{
get { return "Griewank"; }
}
///
/// Minimum possible fitness.
///
public override double MinFitness
{
get { return 0; }
}
///
/// Compute and return fitness for the given parameters.
///
/// Candidate solution.
public override double Fitness(double[] x)
{
Debug.Assert(x != null && x.Length == Dimensionality);
double sum = 0, prod = 1;
for (int i = 0; i < Dimensionality; i++)
{
double elm = x[i];
sum += elm * elm;
prod *= System.Math.Cos(elm / System.Math.Sqrt((double)(i + 1)));
}
return sum / 4000 - prod + 1;
}
///
/// Has the gradient has been implemented?
///
public override bool HasGradient
{
get { return true; }
}
///
/// Compute the gradient of the fitness-function.
///
/// Candidate solution.
/// Array for holding the gradient.
public override int Gradient(double[] x, ref double[] v)
{
Debug.Assert(x != null && x.Length == Dimensionality);
Debug.Assert(v != null && v.Length == Dimensionality);
for (int i = 0; i < Dimensionality; i++)
{
double elm = x[i];
double rec = 1.0 / System.Math.Sqrt((double)(i + 1));
double val2 = System.Math.Sin(elm * rec) * rec;
for (int j = 0; j < Dimensionality; j++)
{
if (i != j)
{
val2 *= System.Math.Cos(x[j] / System.Math.Sqrt((double)(j + 1)));
}
}
v[i] = elm * 1.0 / 2000 + val2;
}
return Dimensionality;
}
#endregion
}
}