#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; namespace HeuristicLab.Problems.TestFunctions.MultiObjective { /// /// The generational Distance is defined as the pth-root of the sum of all d[i]^(p) divided by the size of the front /// where d[i] is the minimal distance the ith point of the evaluated front has to any point in the optimal pareto front. /// p is a dampening factor and is normally set to 1. /// http://shodhganga.inflibnet.ac.in/bitstream/10603/15070/28/28_appendix_h.pdf /// public static class GenerationalDistance { public static double Calculate(IEnumerable front, IEnumerable optimalFront, double p) { if (front == null || optimalFront == null) throw new ArgumentNullException("Fronts must not be null."); if (!front.Any()) throw new ArgumentException("Front must not be empty."); if (p == 0.0) throw new ArgumentException("p must not be 0.0."); double sum = front.Select(r => Math.Pow(Utilities.MinimumDistance(r, optimalFront), p)).Sum(); return Math.Pow(sum, 1 / p) / front.Count(); } } }