#region License Information
/* HeuristicLab
* Copyright (C) 2002-2018 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;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.TestFunctions.MultiObjective;
namespace HeuristicLab.Algorithms.MOCMAEvolutionStrategy {
[Item("HypervolumeIndicator", "Selection of Offspring based on contributing Hypervolume")]
[StorableClass]
internal class HypervolumeIndicator : Item, IIndicator {
#region Constructors and Cloning
[StorableConstructor]
protected HypervolumeIndicator(bool deserializing) : base(deserializing) { }
protected HypervolumeIndicator(HypervolumeIndicator original, Cloner cloner) : base(original, cloner) { }
public override IDeepCloneable Clone(Cloner cloner) { return new HypervolumeIndicator(this, cloner); }
public HypervolumeIndicator() { }
#endregion
public int LeastContributer(IReadOnlyList front, MultiObjectiveBasicProblem problem) {
var frontCopy = front.Select(x => x.PenalizedFitness).ToList();
if (frontCopy.Count <= 1) return 0;
var p = problem as MultiObjectiveTestFunctionProblem;
var refPoint = BuildReferencePoint(p != null ? frontCopy.Concat(new[] { p.ReferencePoint.CloneAsArray() }) : frontCopy, problem.Maximization);
var contributions = Enumerable.Range(0, frontCopy.Count).Select(i => Contribution(frontCopy, i, problem.Maximization, refPoint));
return contributions.Select((value, index) => new { value, index }).OrderBy(x => x.value).First().index;
}
#region Helpers
private static double Contribution(IList front, int idx, bool[] maximization, double[] refPoint) {
var point = front[idx];
front.RemoveAt(idx);
var contribution = -Hypervolume.Calculate(front.ToArray(), refPoint, maximization);
front.Insert(idx, point);
return contribution;
}
private static double[] BuildReferencePoint(IEnumerable front, IReadOnlyList maximization) {
var refPoint = new double[maximization.Count];
foreach (var point in front)
for (var i = 0; i < maximization.Count; i++)
refPoint[i] = maximization[i] ? Math.Min(refPoint[i], point[i]) : Math.Max(refPoint[i], point[i]);
return refPoint;
}
#endregion
}
}