#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 System.Threading; using HeuristicLab.Algorithms.MemPR.Interfaces; using HeuristicLab.Algorithms.MemPR.Util; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.PermutationEncoding; using HeuristicLab.Optimization; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Algorithms.MemPR.Permutation.LocalSearch { [Item("Exhaustive Hillclimber (permutation)", "", ExcludeGenericTypeInfo = true)] [StorableClass] public class ExhaustiveHillClimb : NamedItem, ILocalSearch where TContext : ISingleSolutionHeuristicAlgorithmContext, Encodings.PermutationEncoding.Permutation> { [StorableConstructor] protected ExhaustiveHillClimb(bool deserializing) : base(deserializing) { } protected ExhaustiveHillClimb(ExhaustiveHillClimb original, Cloner cloner) : base(original, cloner) { } public ExhaustiveHillClimb() { Name = ItemName; Description = ItemDescription; } public override IDeepCloneable Clone(Cloner cloner) { return new ExhaustiveHillClimb(this, cloner); } public void Optimize(TContext context) { var evalWrapper = new EvaluationWrapper(context.Problem, context.Solution); var quality = context.Solution.Fitness; try { var result = Exhaustive.HillClimb(context.Random, context.Solution.Solution, ref quality, context.Problem.Maximization, evalWrapper.Evaluate, CancellationToken.None); context.IncrementEvaluatedSolutions(result.Item1); context.Iterations = result.Item2; } finally { context.Solution.Fitness = quality; } } } }