#region License Information
/* HeuristicLab
* Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Operators;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Algorithms.ParticleSwarmOptimization {
[Item("Multi PSO Topology Updater", "Splits swarm into NrOfSwarms 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.")]
[StorableClass]
public sealed class MultiPSOTopologyUpdater : SingleSuccessorOperator, ITopologyUpdater {
public override bool CanChangeName {
get { return false; }
}
#region Parameters
public ILookupParameter RandomParameter {
get { return (ILookupParameter)Parameters["Random"]; }
}
public IValueLookupParameter NrOfSwarmsParameter {
get { return (IValueLookupParameter)Parameters["NrOfSwarms"]; }
}
public ILookupParameter SwarmSizeParameter {
get { return (ILookupParameter)Parameters["SwarmSize"]; }
}
public IScopeTreeLookupParameter NeighborsParameter {
get { return (IScopeTreeLookupParameter)Parameters["Neighbors"]; }
}
public ILookupParameter CurrentIterationParameter {
get { return (ILookupParameter)Parameters["CurrentIteration"]; }
}
public IValueLookupParameter RegroupingPeriodParameter {
get { return (IValueLookupParameter)Parameters["RegroupingPeriod"]; }
}
#endregion
#region Parameter Values
private IRandom Random {
get { return RandomParameter.ActualValue; }
}
private int NrOfSwarms {
get { return NrOfSwarmsParameter.ActualValue.Value; }
}
private int SwarmSize {
get { return SwarmSizeParameter.ActualValue.Value; }
}
private ItemArray Neighbors {
get { return NeighborsParameter.ActualValue; }
set { NeighborsParameter.ActualValue = value; }
}
private int CurrentIteration {
get { return CurrentIterationParameter.ActualValue.Value; }
}
private int RegroupingPeriod {
get { return RegroupingPeriodParameter.ActualValue.Value; }
}
#endregion
#region Construction & Cloning
[StorableConstructor]
private MultiPSOTopologyUpdater(bool deserializing) : base(deserializing) { }
private MultiPSOTopologyUpdater(MultiPSOTopologyUpdater original, Cloner cloner) : base(original, cloner) { }
public MultiPSOTopologyUpdater()
: base() {
Parameters.Add(new LookupParameter("Random", "A random number generator."));
Parameters.Add(new ValueLookupParameter("NrOfSwarms", "Nr of connected sub-swarms.", new IntValue(3)));
Parameters.Add(new LookupParameter("SwarmSize", "Number of particles in the swarm."));
Parameters.Add(new ScopeTreeLookupParameter("Neighbors", "The list of neighbors for each particle."));
Parameters.Add(new LookupParameter("CurrentIteration", "The current iteration of the algorithm."));
Parameters.Add(new ValueLookupParameter("RegroupingPeriod", "Update interval (=iterations) for regrouping of neighborhoods.", new IntValue(5)));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new MultiPSOTopologyUpdater(this, cloner);
}
#endregion
public override IOperation Apply() {
if (CurrentIteration > 0 && CurrentIteration % RegroupingPeriod == 0) {
ItemArray neighbors = new ItemArray(SwarmSize);
var particles = Enumerable.Range(0, SwarmSize).ToList();
for (int i = SwarmSize-1; i>0; i--) {
int j = Random.Next(i+1);
int t = particles[j];
particles[j] = particles[i];
particles[i] = t;
}
for (int partitionNr = 0; partitionNr list, int start, int end, int excludedIndex) {
return new IntArray(list
.Skip(start)
.Take(end-start)
.Where((p, j) => start+j != excludedIndex)
.ToArray());
}
}
}