#region License Information /* HeuristicLab * Copyright (C) 2002-2015 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.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.BinaryVectorEncoding; using HeuristicLab.Operators; using HeuristicLab.Optimization; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.Knapsack { /// /// An operator for analyzing the best solution for a Knapsack problem. /// [Item("BestKnapsackSolutionAnalyzer", "An operator for analyzing the best solution for a Knapsack problem.")] [StorableClass] public class BestKnapsackSolutionAnalyzer : SingleSuccessorOperator, IBinaryVectorSolutionsOperator, IAnalyzer, ISingleObjectiveOperator { public virtual bool EnabledByDefault { get { return true; } } public ILookupParameter MaximizationParameter { get { return (ILookupParameter)Parameters["Maximization"]; } } public IScopeTreeLookupParameter BinaryVectorsParameter { get { return (IScopeTreeLookupParameter)Parameters["BinaryVectors"]; } } public ILookupParameter KnapsackCapacityParameter { get { return (ILookupParameter)Parameters["KnapsackCapacity"]; } } public ILookupParameter WeightsParameter { get { return (ILookupParameter)Parameters["Weights"]; } } public ILookupParameter ValuesParameter { get { return (ILookupParameter)Parameters["Values"]; } } public IScopeTreeLookupParameter QualityParameter { get { return (IScopeTreeLookupParameter)Parameters["Quality"]; } } public ILookupParameter BestSolutionParameter { get { return (ILookupParameter)Parameters["BestKnapsackSolution"]; } } public IValueLookupParameter ResultsParameter { get { return (IValueLookupParameter)Parameters["Results"]; } } public ILookupParameter BestKnownQualityParameter { get { return (ILookupParameter)Parameters["BestKnownQuality"]; } } public ILookupParameter BestKnownSolutionParameter { get { return (ILookupParameter)Parameters["BestKnownSolution"]; } } [StorableConstructor] protected BestKnapsackSolutionAnalyzer(bool deserializing) : base(deserializing) { } protected BestKnapsackSolutionAnalyzer(BestKnapsackSolutionAnalyzer original, Cloner cloner) : base(original, cloner) { } public BestKnapsackSolutionAnalyzer() : base() { Parameters.Add(new LookupParameter("Maximization", "True if the problem is a maximization problem.")); Parameters.Add(new ScopeTreeLookupParameter("BinaryVectors", "The Knapsack solutions from which the best solution should be visualized.")); Parameters.Add(new LookupParameter("KnapsackCapacity", "Capacity of the Knapsack.")); Parameters.Add(new LookupParameter("Weights", "The weights of the items.")); Parameters.Add(new LookupParameter("Values", "The values of the items.")); Parameters.Add(new ScopeTreeLookupParameter("Quality", "The qualities of the Knapsack solutions which should be visualized.")); Parameters.Add(new LookupParameter("BestKnapsackSolution", "The best Knapsack solution.")); Parameters.Add(new ValueLookupParameter("Results", "The result collection where the knapsack solution should be stored.")); Parameters.Add(new LookupParameter("BestKnownQuality", "The quality of the best known solution.")); Parameters.Add(new LookupParameter("BestKnownSolution", "The best known solution.")); } public override IDeepCloneable Clone(Cloner cloner) { return new BestKnapsackSolutionAnalyzer(this, cloner); } public override IOperation Apply() { var binaryVectors = BinaryVectorsParameter.ActualValue; var qualities = QualityParameter.ActualValue; var results = ResultsParameter.ActualValue; var max = MaximizationParameter.ActualValue.Value; var bestKnownQuality = BestKnownQualityParameter.ActualValue; int i = -1; i = !max ? qualities.Select((x, index) => new { index, x.Value }).OrderBy(x => x.Value).First().index : qualities.Select((x, index) => new { index, x.Value }).OrderByDescending(x => x.Value).First().index; if (bestKnownQuality == null || max && qualities[i].Value > bestKnownQuality.Value || !max && qualities[i].Value < bestKnownQuality.Value) { BestKnownQualityParameter.ActualValue = new DoubleValue(qualities[i].Value); BestKnownSolutionParameter.ActualValue = (BinaryVector)binaryVectors[i].Clone(); } var solution = BestSolutionParameter.ActualValue; if (solution == null) { solution = new KnapsackSolution((BinaryVector)binaryVectors[i].Clone(), new DoubleValue(qualities[i].Value), KnapsackCapacityParameter.ActualValue, WeightsParameter.ActualValue, ValuesParameter.ActualValue); BestSolutionParameter.ActualValue = solution; results.Add(new Result("Best Knapsack Solution", solution)); } else { if (max && qualities[i].Value > solution.Quality.Value || !max && qualities[i].Value < solution.Quality.Value) { solution.BinaryVector = (BinaryVector)binaryVectors[i].Clone(); solution.Quality = new DoubleValue(qualities[i].Value); solution.Capacity = KnapsackCapacityParameter.ActualValue; solution.Weights = WeightsParameter.ActualValue; solution.Values = ValuesParameter.ActualValue; } } return base.Apply(); } } }