#region License Information /* HeuristicLab * Copyright (C) 2002-2016 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.Data; using HeuristicLab.Optimization; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.MultiObjectiveTestFunctions { [StorableClass] [Item("GenerationalDistanceAnalyzer", "The generational distance between the current and the best known front (see Multi-Objective Performance Metrics - Shodhganga for more information)")] public class GenerationalDistanceAnalyzer : MOTFAnalyzer { [StorableHook(HookType.AfterDeserialization)] private void AfterDeserialization() { } [StorableConstructor] protected GenerationalDistanceAnalyzer(bool deserializing) : base(deserializing) { } public GenerationalDistanceAnalyzer(GenerationalDistanceAnalyzer original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new GenerationalDistanceAnalyzer(this, cloner); } /// /// private IValueParameter DampeningParameter { get { return (IValueParameter)Parameters["Dampening"]; } set { Parameters["Dampening"].ActualValue = value; } } public GenerationalDistanceAnalyzer() { Parameters.Add(new ValueParameter("Dampening", "", new DoubleValue(1))); } public override void Analyze(Individual[] individuals, double[][] qualities, ResultCollection results) { int objectives = qualities[0].Length; var optimalfront = TestFunctionParameter.ActualValue.OptimalParetoFront(objectives); if (optimalfront == null) return; if (!results.ContainsKey("GenerationalDistance")) results.Add(new Result("GenerationalDistance", typeof(DoubleValue))); results["GenerationalDistance"].Value = new DoubleValue(GenerationalDistance.Calculate(qualities, optimalfront, DampeningParameter.Value.Value)); } } }