[3150] | 1 | #region License Information
|
---|
| 2 | /* HeuristicLab
|
---|
[17180] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
|
---|
[3150] | 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 |
|
---|
| 22 | using System;
|
---|
[4722] | 23 | using HeuristicLab.Common;
|
---|
[3150] | 24 | using HeuristicLab.Core;
|
---|
[3154] | 25 | using HeuristicLab.Data;
|
---|
[3150] | 26 | using HeuristicLab.Encodings.RealVectorEncoding;
|
---|
[16565] | 27 | using HEAL.Attic;
|
---|
[3150] | 28 |
|
---|
[3170] | 29 | namespace HeuristicLab.Problems.TestFunctions {
|
---|
[3150] | 30 | /// <summary>
|
---|
[3315] | 31 | /// The Ackley function as described in Eiben, A.E. and Smith, J.E. 2003. Introduction to Evolutionary Computation. Natural Computing Series, Springer-Verlag Berlin Heidelberg
|
---|
| 32 | /// is highly multimodal. It has a single global minimum at the origin with value 0.
|
---|
[3150] | 33 | /// </summary
|
---|
[3315] | 34 | [Item("AckleyEvaluator", "Evaluates the Ackley function 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.")]
|
---|
[16565] | 35 | [StorableType("AB46422C-A915-4BC5-B2F7-F6F4794E87D2")]
|
---|
[3170] | 36 | public class AckleyEvaluator : SingleObjectiveTestFunctionProblemEvaluator {
|
---|
[9980] | 37 | public override string FunctionName { get { return "Ackley"; } }
|
---|
[3150] | 38 | /// <summary>
|
---|
| 39 | /// Returns false as the Ackley function is a minimization problem.
|
---|
| 40 | /// </summary>
|
---|
| 41 | public override bool Maximization {
|
---|
| 42 | get { return false; }
|
---|
| 43 | }
|
---|
| 44 | /// <summary>
|
---|
[3154] | 45 | /// Gets the optimum function value (0).
|
---|
| 46 | /// </summary>
|
---|
| 47 | public override double BestKnownQuality {
|
---|
| 48 | get { return 0; }
|
---|
| 49 | }
|
---|
| 50 | /// <summary>
|
---|
[3150] | 51 | /// Gets the lower and upper bound of the function.
|
---|
| 52 | /// </summary>
|
---|
| 53 | public override DoubleMatrix Bounds {
|
---|
| 54 | get { return new DoubleMatrix(new double[,] { { -32.768, 32.768 } }); }
|
---|
| 55 | }
|
---|
| 56 | /// <summary>
|
---|
| 57 | /// Gets the minimum problem size (1).
|
---|
| 58 | /// </summary>
|
---|
| 59 | public override int MinimumProblemSize {
|
---|
| 60 | get { return 1; }
|
---|
| 61 | }
|
---|
| 62 | /// <summary>
|
---|
| 63 | /// Gets the (theoretical) maximum problem size (2^31 - 1).
|
---|
| 64 | /// </summary>
|
---|
| 65 | public override int MaximumProblemSize {
|
---|
| 66 | get { return int.MaxValue; }
|
---|
| 67 | }
|
---|
| 68 |
|
---|
[4722] | 69 | [StorableConstructor]
|
---|
[16565] | 70 | protected AckleyEvaluator(StorableConstructorFlag _) : base(_) { }
|
---|
[4722] | 71 | protected AckleyEvaluator(AckleyEvaluator original, Cloner cloner) : base(original, cloner) { }
|
---|
| 72 | public AckleyEvaluator() : base() { }
|
---|
| 73 |
|
---|
| 74 | public override IDeepCloneable Clone(Cloner cloner) {
|
---|
| 75 | return new AckleyEvaluator(this, cloner);
|
---|
| 76 | }
|
---|
| 77 |
|
---|
[3781] | 78 | public override RealVector GetBestKnownSolution(int dimension) {
|
---|
| 79 | return new RealVector(dimension);
|
---|
| 80 | }
|
---|
| 81 |
|
---|
[3150] | 82 | /// <summary>
|
---|
| 83 | /// Evaluates the Ackley function for a specific <paramref name="point"/>.
|
---|
| 84 | /// </summary>
|
---|
| 85 | /// <param name="point">N-dimensional point for which the test function should be evaluated.</param>
|
---|
| 86 | /// <returns>The result value of the Ackley function at the given point.</returns>
|
---|
| 87 | public static double Apply(RealVector point) {
|
---|
[3450] | 88 | double result;
|
---|
[3150] | 89 | double val;
|
---|
| 90 |
|
---|
| 91 | val = 0;
|
---|
| 92 | for (int i = 0; i < point.Length; i++)
|
---|
| 93 | val += point[i] * point[i];
|
---|
[3315] | 94 | val /= point.Length;
|
---|
| 95 | val = -0.2 * Math.Sqrt(val);
|
---|
[3450] | 96 | result = 20 - 20 * Math.Exp(val);
|
---|
[3150] | 97 |
|
---|
| 98 | val = 0;
|
---|
| 99 | for (int i = 0; i < point.Length; i++)
|
---|
| 100 | val += Math.Cos(2 * Math.PI * point[i]);
|
---|
[3315] | 101 | val /= point.Length;
|
---|
[3450] | 102 | result += Math.E - Math.Exp(val);
|
---|
[3150] | 103 | return (result);
|
---|
| 104 | }
|
---|
| 105 |
|
---|
| 106 | /// <summary>
|
---|
| 107 | /// Evaluates the test function for a specific <paramref name="point"/>.
|
---|
| 108 | /// </summary>
|
---|
| 109 | /// <remarks>Calls <see cref="Apply"/>.</remarks>
|
---|
| 110 | /// <param name="point">N-dimensional point for which the test function should be evaluated.</param>
|
---|
| 111 | /// <returns>The result value of the Ackley function at the given point.</returns>
|
---|
[9407] | 112 | public override double Evaluate(RealVector point) {
|
---|
[3150] | 113 | return Apply(point);
|
---|
| 114 | }
|
---|
| 115 | }
|
---|
| 116 | }
|
---|