#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));
}
}
}