1 | using System;
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2 | using System.Collections.Generic;
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3 | using System.Linq;
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4 | using System.Text;
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5 | using HeuristicLab.Core;
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6 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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7 | using HeuristicLab.Operators;
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8 | using HeuristicLab.Common;
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9 | using HeuristicLab.Parameters;
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10 | using HeuristicLab.Encodings.IntegerVectorEncoding;
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11 | using HeuristicLab.Data;
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12 |
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13 | namespace HeuristicLab.Algorithms.ParticleSwarmOptimization {
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14 | [Item("Multi PSO Topology Initializer/Updater", "Splits swarm into swarmsize / (nrOfConnections + 1) non-overlapping sub-swarms. Swarms are re-grouped every regroupingPeriod iteration. The operator is implemented as described in Liang, J.J. and Suganthan, P.N 2005. Dynamic multi-swarm particle swarm optimizer. IEEE Swarm Intelligence Symposium, pp. 124-129")]
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15 | [StorableClass]
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16 | public class MultiPSOTopologyUpdater : SingleSuccessorOperator, ITopologyUpdater, ITopologyInitializer {
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17 | #region Parameters
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18 | public ILookupParameter<IRandom> RandomParameter {
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19 | get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
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20 | }
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21 | public IValueLookupParameter<IntValue> NrOfConnectionsParameter {
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22 | get { return (IValueLookupParameter<IntValue>)Parameters["NrOfConnections"]; }
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23 | }
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24 | public ILookupParameter<IntValue> SwarmSizeParameter {
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25 | get { return (ILookupParameter<IntValue>)Parameters["SwarmSize"]; }
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26 | }
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27 | public IScopeTreeLookupParameter<IntegerVector> NeighborsParameter {
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28 | get { return (IScopeTreeLookupParameter<IntegerVector>)Parameters["Neighbors"]; }
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29 | }
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30 | public ILookupParameter<IntValue> CurrentIterationParameter {
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31 | get { return (ILookupParameter<IntValue>)Parameters["CurrentIteration"]; }
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32 | }
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33 | public IValueLookupParameter<IntValue> RegroupingPeriodParameter {
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34 | get { return (IValueLookupParameter<IntValue>)Parameters["RegroupingPeriod"]; }
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35 | }
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36 | #endregion
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37 |
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38 | #region Parameter Values
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39 | public IRandom Random {
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40 | get { return RandomParameter.ActualValue; }
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41 | }
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42 | public int NrOfConnections {
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43 | get { return NrOfConnectionsParameter.ActualValue.Value; }
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44 | }
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45 | public int SwarmSize {
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46 | get { return SwarmSizeParameter.ActualValue.Value; }
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47 | }
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48 | public ItemArray<IntegerVector> Neighbors {
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49 | get { return NeighborsParameter.ActualValue; }
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50 | set { NeighborsParameter.ActualValue = value; }
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51 | }
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52 | public int CurrentIteration {
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53 | get { return CurrentIterationParameter.ActualValue.Value; }
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54 | }
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55 | public int RegroupingPeriod {
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56 | get { return RegroupingPeriodParameter.ActualValue.Value; }
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57 | }
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58 | #endregion
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59 |
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60 | public MultiPSOTopologyUpdater()
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61 | : base() {
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62 | Parameters.Add(new LookupParameter<IRandom>("Random", "A random number generator."));
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63 | Parameters.Add(new ValueLookupParameter<IntValue>("NrOfConnections", "Nr of connected neighbors.", new IntValue(3)));
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64 | Parameters.Add(new LookupParameter<IntValue>("SwarmSize", "Number of particles in the swarm."));
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65 | Parameters.Add(new ScopeTreeLookupParameter<IntegerVector>("Neighbors", "The list of neighbors for each particle."));
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66 | Parameters.Add(new LookupParameter<IntValue>("CurrentIteration", "The current iteration of the algorithm."));
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67 | Parameters.Add(new ValueLookupParameter<IntValue>("RegroupingPeriod", "Update interval (=iterations) for regrouping of neighborhoods.", new IntValue(5)));
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68 | }
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69 |
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70 | // Splits the swarm into non-overlapping sub swarms
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71 | public override IOperation Apply() {
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72 | if (CurrentIteration % RegroupingPeriod == 0) {
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73 | ItemArray<IntegerVector> neighbors = new ItemArray<IntegerVector>(SwarmSize);
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74 | Dictionary<int, List<int>> neighborsPerParticle = new Dictionary<int, List<int>>();
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75 | for (int i = 0; i < SwarmSize; i++) {
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76 | neighborsPerParticle.Add(i, new List<int>());
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77 | }
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78 |
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79 | // partition swarm into groups
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80 | Dictionary<int, List<int>> groups = new Dictionary<int, List<int>>();
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81 | int groupId = 0;
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82 | var numbers = Enumerable.Range(0, SwarmSize).ToList();
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83 | for (int i = 0; i < SwarmSize; i++) {
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84 | int nextParticle = numbers[Random.Next(0, numbers.Count)];
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85 | if (!groups.ContainsKey(groupId)) {
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86 | groups.Add(groupId, new List<int>());
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87 | }
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88 | groups[groupId].Add(nextParticle);
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89 | if (groups[groupId].Count - 1 == NrOfConnections) {
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90 | groupId++;
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91 | }
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92 | numbers.Remove(nextParticle);
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93 | }
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94 |
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95 | // add neighbors to each particle
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96 | foreach (List<int> group in groups.Values) {
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97 | foreach (int sib1 in group) {
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98 | foreach (int sib2 in group) {
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99 | if (sib1 != sib2 && !neighborsPerParticle[sib1].Contains(sib2)) {
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100 | neighborsPerParticle[sib1].Add(sib2);
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101 | }
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102 | }
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103 | }
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104 | }
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105 |
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106 | for (int particle = 0; particle < neighborsPerParticle.Count; particle++) {
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107 | neighbors[particle] = new IntegerVector(neighborsPerParticle[particle].ToArray());
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108 | }
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109 | Neighbors = neighbors;
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110 | }
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111 | return base.Apply();
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112 | }
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113 |
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114 | [StorableConstructor]
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115 | protected MultiPSOTopologyUpdater(bool deserializing) : base(deserializing) { }
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116 |
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117 | #region Cloning
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118 | public override IDeepCloneable Clone(Cloner cloner) {
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119 | return new MultiPSOTopologyUpdater(this, cloner);
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120 | }
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121 |
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122 | protected MultiPSOTopologyUpdater(MultiPSOTopologyUpdater original, Cloner cloner)
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123 | : base(original, cloner) {
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124 | }
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125 | #endregion
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126 | }
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127 | }
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