[11450] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[12012] | 3 | * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[11450] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.IO;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Algorithms.ParticleSwarmOptimization;
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| 26 | using HeuristicLab.Data;
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| 27 | using HeuristicLab.Encodings.RealVectorEncoding;
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| 28 | using HeuristicLab.Optimization.Operators;
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| 29 | using HeuristicLab.Persistence.Default.Xml;
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| 30 | using HeuristicLab.Problems.TestFunctions;
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| 31 | using Microsoft.VisualStudio.TestTools.UnitTesting;
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| 32 |
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| 33 | namespace HeuristicLab.Tests {
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| 34 | [TestClass]
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| 35 | public class PsoSchwefelSampleTest {
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[11514] | 36 | private const string SampleFileName = "PSO_Schwefel";
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[11450] | 37 |
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| 38 | [TestMethod]
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| 39 | [TestCategory("Samples.Create")]
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| 40 | [TestProperty("Time", "medium")]
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| 41 | public void CreatePsoSchwefelSampleTest() {
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| 42 | var pso = CreatePsoSchwefelSample();
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[11514] | 43 | string path = Path.Combine(SamplesUtils.SamplesDirectory, SampleFileName + SamplesUtils.SampleFileExtension);
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| 44 | XmlGenerator.Serialize(pso, path);
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[11450] | 45 | }
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| 46 | [TestMethod]
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| 47 | [TestCategory("Samples.Execute")]
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| 48 | [TestProperty("Time", "medium")]
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| 49 | public void RunPsoSchwefelSampleTest() {
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| 50 | var pso = CreatePsoSchwefelSample();
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| 51 | pso.SetSeedRandomly.Value = false;
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| 52 | SamplesUtils.RunAlgorithm(pso);
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| 53 | if (!Environment.Is64BitProcess) {
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| 54 | Assert.AreEqual(118.44027985932837, SamplesUtils.GetDoubleResult(pso, "BestQuality"));
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| 55 | Assert.AreEqual(140.71570105946438, SamplesUtils.GetDoubleResult(pso, "CurrentAverageQuality"));
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| 56 | Assert.AreEqual(220.956806502853, SamplesUtils.GetDoubleResult(pso, "CurrentWorstQuality"));
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| 57 | Assert.AreEqual(1000, SamplesUtils.GetIntResult(pso, "Iterations"));
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| 58 | } else {
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| 59 | Assert.AreEqual(118.43958282879345, SamplesUtils.GetDoubleResult(pso, "BestQuality"));
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| 60 | Assert.AreEqual(139.43946864779372, SamplesUtils.GetDoubleResult(pso, "CurrentAverageQuality"));
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| 61 | Assert.AreEqual(217.14654589055152, SamplesUtils.GetDoubleResult(pso, "CurrentWorstQuality"));
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| 62 | Assert.AreEqual(1000, SamplesUtils.GetIntResult(pso, "Iterations"));
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| 63 | }
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| 64 | }
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| 65 |
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| 66 | private ParticleSwarmOptimization CreatePsoSchwefelSample() {
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| 67 | ParticleSwarmOptimization pso = new ParticleSwarmOptimization();
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| 68 | #region Problem Configuration
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| 69 | var problem = new SingleObjectiveTestFunctionProblem();
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| 70 | problem.BestKnownQuality.Value = 0.0;
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| 71 | problem.BestKnownSolutionParameter.Value = new RealVector(new double[] { 420.968746, 420.968746 });
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| 72 | problem.Bounds = new DoubleMatrix(new double[,] { { -500, 500 } });
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| 73 | problem.EvaluatorParameter.Value = new SchwefelEvaluator();
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| 74 | problem.Maximization.Value = false;
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| 75 | problem.ProblemSize.Value = 2;
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| 76 | problem.SolutionCreatorParameter.Value = new UniformRandomRealVectorCreator();
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| 77 | #endregion
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| 78 | #region Algorithm Configuration
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| 79 | pso.Name = "Particle Swarm Optimization - Schwefel";
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| 80 | 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)";
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| 81 | pso.Problem = problem;
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| 82 | pso.Inertia.Value = 10;
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| 83 | pso.MaxIterations.Value = 1000;
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| 84 | pso.NeighborBestAttraction.Value = 0.5;
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| 85 | pso.PersonalBestAttraction.Value = -0.01;
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| 86 | pso.SwarmSize.Value = 50;
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| 87 |
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| 88 | var inertiaUpdater = pso.InertiaUpdaterParameter.ValidValues
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| 89 | .OfType<ExponentialDiscreteDoubleValueModifier>()
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| 90 | .Single();
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| 91 | inertiaUpdater.StartValueParameter.Value = new DoubleValue(10);
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| 92 | inertiaUpdater.EndValueParameter.Value = new DoubleValue(1);
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| 93 | pso.InertiaUpdater = inertiaUpdater;
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| 94 |
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| 95 | pso.ParticleCreator = pso.ParticleCreatorParameter.ValidValues
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| 96 | .OfType<RealVectorParticleCreator>()
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| 97 | .Single();
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| 98 | var swarmUpdater = pso.SwarmUpdaterParameter.ValidValues
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| 99 | .OfType<RealVectorSwarmUpdater>()
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| 100 | .Single();
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| 101 | swarmUpdater.VelocityBoundsIndexParameter.ActualName = "Iterations";
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| 102 | swarmUpdater.VelocityBoundsParameter.Value = new DoubleMatrix(new double[,] { { -10, 10 } });
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| 103 | swarmUpdater.VelocityBoundsStartValueParameter.Value = new DoubleValue(10.0);
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| 104 | swarmUpdater.VelocityBoundsEndValueParameter.Value = new DoubleValue(1.0);
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| 105 | swarmUpdater.VelocityBoundsScalingOperatorParameter.Value = swarmUpdater.VelocityBoundsScalingOperatorParameter.ValidValues
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| 106 | .OfType<ExponentialDiscreteDoubleValueModifier>()
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| 107 | .Single();
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| 108 |
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| 109 | pso.TopologyInitializer = null;
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| 110 | pso.TopologyUpdater = null;
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| 111 | pso.SwarmUpdater = swarmUpdater;
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| 112 | pso.Seed.Value = 0;
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| 113 | pso.SetSeedRandomly.Value = true;
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| 114 | #endregion
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| 115 | pso.Engine = new ParallelEngine.ParallelEngine();
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| 116 | return pso;
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| 117 | }
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| 118 | }
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| 119 | }
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