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source: branches/ProblemRefactoring/HeuristicLab.Problems.TestFunctions/3.3/Functions/SchafferF6.cs @ 14815

Last change on this file since 14815 was 13405, checked in by abeham, 9 years ago

#2521: Implemented SchafferF6 test function

File size: 4.3 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using HeuristicLab.Common;
24using HeuristicLab.Core;
25using HeuristicLab.Data;
26using HeuristicLab.Encodings.RealVectorEncoding;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.TestFunctions {
30  /// <summary>
31  /// The generalized Rastrigin function y = 0.5 + (Sin^2(Sqrt(x^2 + y^2)) - 0.5) / (1 + 0.001 * (x^2 + y^2))^2 is a multimodal function that has its optimal value 0 at the origin.
32
33  /// </summary
34  [Item("SchafferF6", "Evaluates the Schaffer F6 function y = 0.5 + (Sin^2(Sqrt(x^2 + y^2)) - 0.5) / (1 + 0.001 * (x^2 + y^2))^2 on a given point. The optimum of this function is 0 at the origin. It is implemented as described in Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg.")]
35  [StorableClass]
36  public class SchafferF6 : SingleObjectiveTestFunction {
37    /// <summary>
38    /// Returns false as the Rastrigin function is a minimization problem.
39    /// </summary>
40    public override bool Maximization {
41      get { return false; }
42    }
43    /// <summary>
44    /// Gets the optimum function value (0).
45    /// </summary>
46    public override double BestKnownQuality {
47      get { return 0; }
48    }
49    /// <summary>
50    /// Gets the lower and upper bound of the function.
51    /// </summary>
52    public override DoubleMatrix Bounds {
53      get { return new DoubleMatrix(new double[,] { { -100, 100 } }); }
54    }
55    /// <summary>
56    /// Gets the minimum problem size (2).
57    /// </summary>
58    public override int MinimumProblemSize {
59      get { return 2; }
60    }
61    /// <summary>
62    /// Gets the maximum problem size (2).
63    /// </summary>
64    public override int MaximumProblemSize {
65      get { return 2; }
66    }
67
68    public override RealVector GetBestKnownSolution(int dimension) {
69      return new RealVector(dimension);
70    }
71
72    [StorableConstructor]
73    protected SchafferF6(bool deserializing) : base(deserializing) { }
74    protected SchafferF6(SchafferF6 original, Cloner cloner) : base(original, cloner) { }
75    public SchafferF6() : base() { }
76
77    public override IDeepCloneable Clone(Cloner cloner) {
78      return new SchafferF6(this, cloner);
79    }
80
81    /// <summary>
82    /// Evaluates the test function for a specific <paramref name="point"/>.
83    /// </summary>
84    /// <param name="point">N-dimensional point for which the test function should be evaluated.</param>
85    /// <returns>The result value of the Rastrigin function at the given point.</returns>
86    public static double Apply(RealVector point) {
87      if (point.Length != 2) throw new ArgumentException("The SchafferF6 can only be evaluated for two dimenional vectors");
88      var sumSquare = point[0] * point[0] + point[1] * point[1];
89      var sin = Math.Sin(Math.Sqrt(sumSquare));
90      var nom = sin * sin - 0.5;
91      var denom = (1 + 0.001 * sumSquare) * (1 + 0.001 * sumSquare);
92      return 0.5 + nom / denom;
93    }
94
95    /// <summary>
96    /// Evaluates the test function for a specific <paramref name="point"/>.
97    /// </summary>
98    /// <remarks>Calls <see cref="Apply"/>.</remarks>
99    /// <param name="point">N-dimensional point for which the test function should be evaluated.</param>
100    /// <returns>The result value of the Rastrigin function at the given point.</returns>
101    public override double Evaluate(RealVector point) {
102      return Apply(point);
103    }
104  }
105}
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