/// ------------------------------------------------------ /// 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 } }