#region License Information /* HeuristicLab * Copyright (C) 2002-2011 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 HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.RealVectorEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Algorithms.ParticleSwarmOptimization { [Item("Totally Connected Particle Updater", "Updates the particle's position using (among other things) the global best position. Use together with the empty topology initialzer. Point = Point + Velocity*Omega + (PersonalBestPoint-Point)*Phi_P*r_p + (BestPoint-Point)*Phi_G*r_g")] [StorableClass] public sealed class TotallyConnectedParticleUpdater : ParticleUpdater, IGlobalParticleUpdater { #region Construction & Cloning [StorableConstructor] private TotallyConnectedParticleUpdater(bool deserializing) : base(deserializing) { } private TotallyConnectedParticleUpdater(TotallyConnectedParticleUpdater original, Cloner cloner) : base(original, cloner) { } public TotallyConnectedParticleUpdater() : base() { } public override IDeepCloneable Clone(Cloner cloner) { return new TotallyConnectedParticleUpdater(this, cloner); } #endregion public override IOperation Apply() { base.Apply(); RealVector velocity = new RealVector(Velocity.Length); RealVector position = new RealVector(Point.Length); double r_p = Random.NextDouble(); double r_g = Random.NextDouble(); double omega = Omega.Value; double phi_p = Phi_P.Value; double phi_g = Phi_G.Value; for (int i = 0; i < velocity.Length; i++) { velocity[i] = Velocity[i] * omega + (PersonalBestPoint[i] - Point[i]) * phi_p * r_p + (BestPoint[i] - Point[i]) * phi_g * r_g; } BoundsChecker.Apply(velocity, VelocityBounds); for (int i = 0; i < velocity.Length; i++) { position[i] = Point[i] + velocity[i]; } BoundsChecker.Apply(position, Bounds); Point = position; Velocity = velocity; return base.Apply(); } } }