source: branches/OptimizationNetworks/HeuristicLab.Networks.IntegratedOptimization.TravelingThief/3.3/TtpOrchestratorNode3.cs @ 14616

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2017 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Core.Networks;
using HeuristicLab.Data;
using HeuristicLab.Encodings.BinaryVectorEncoding;
using HeuristicLab.Encodings.PermutationEncoding;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.Knapsack;
using HeuristicLab.Problems.TravelingSalesman;

namespace HeuristicLab.Networks.IntegratedOptimization.TravelingThief {
  [Item("TtpOrchestratorNode3", "Orchestrator for TTP optimization network version 3.")]
  [StorableClass]
  public sealed class TtpOrchestratorNode3 : TtpOrchestratorNode {
    [StorableConstructor]
    private TtpOrchestratorNode3(bool deserializing) : base(deserializing) { }
    private TtpOrchestratorNode3(TtpOrchestratorNode3 original, Cloner cloner) : base(original, cloner) { }
    public TtpOrchestratorNode3() : this("TtpOrchestratorNode3") { }
    public TtpOrchestratorNode3(string name) : base(name) {
      MetaSolverOrchestrationPort = CreateOrchestrationPort<VariegationProblem>(MetaSolverName);
      MetaSolverEvaluationPort = CreateEvaluationPort<RealVector>(MetaSolverName, "RealVector", "Quality");
      TspSolverOrchestrationPort = CreateOrchestrationPort<TravelingSalesmanProblem>(TspSolverName);
      KspSolverOrchestrationPort = CreateOrchestrationPort<BinaryKnapsackProblem>(KspSolverName);
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new TtpOrchestratorNode3(this, cloner);
    }

    public override void Prepare(bool clearRuns = false) {
      base.Prepare(clearRuns);

      var metaMsg = MetaSolverOrchestrationPort.PrepareMessage();
      var msgFlags = OrchestrationMessage.Prepare | OrchestrationMessage.SetEvalHook;
      if (clearRuns) msgFlags |= OrchestrationMessage.ClearRuns;
      metaMsg["OrchestrationMessage"] = new EnumValue<OrchestrationMessage>(msgFlags);
      var problem = new VariegationProblem();
      problem.Encoding.Length = KspParameter.Value.Length + TspParameter.Value.Coordinates.Rows * 2;
      problem.Encoding.Bounds = new DoubleMatrix(new[,] { { -1.0, 1.0 } });
      metaMsg["Problem"] = problem;
      MetaSolverOrchestrationPort.SendMessage(metaMsg);
    }

    #region MetaSolver Message Handling
    protected override void MetaSolverEvaluationPortMessage(IMessage message) {
      var factors = (RealVector)message["RealVector"];
      int fi = 0;

      var ksp = (BinaryKnapsackProblem)KspParameter.Value.Clone();
      while (fi < ksp.Values.Length) {
        ksp.Values[fi] = (int)Math.Ceiling(ksp.Values[fi] * factors[fi]);
        ++fi;
      }

      var kspMsg = KspSolverOrchestrationPort.PrepareMessage();
      kspMsg["OrchestrationMessage"] = new EnumValue<OrchestrationMessage>(OrchestrationMessage.Prepare | OrchestrationMessage.ClearRuns | OrchestrationMessage.Start);
      kspMsg["Problem"] = ksp;
      KspSolverOrchestrationPort.SendMessage(kspMsg);
      cts.Token.ThrowIfCancellationRequested();

      var bestKspSolution = (BinaryVector)kspResults["Best Solution"].Value.Clone();
      var kspCapacity = (IntValue)KspParameter.Value.KnapsackCapacity.Clone();
      var kspPenalty = new DoubleValue(0.0);
      var kspWeights = (IntArray)KspParameter.Value.Weights.Clone();
      var kspValues = (IntArray)KspParameter.Value.Values.Clone();
      var bestKspQuality = KnapsackEvaluator.Apply(bestKspSolution, kspCapacity, kspPenalty, kspWeights, kspValues).Quality;
      var loot = new KnapsackSolution(bestKspSolution, bestKspQuality, kspCapacity, kspWeights, kspValues);

      var tsp = (TravelingSalesmanProblem)TspParameter.Value.Clone();
      for (int j = 0; j < tsp.Coordinates.Rows; j++) {
        tsp.Coordinates[j, 0] = (int)Math.Ceiling(tsp.Coordinates[j, 0] * factors[fi + j * 2]);
        tsp.Coordinates[j, 1] = (int)Math.Ceiling(tsp.Coordinates[j, 1] * factors[fi + j * 2 + 1]);
      }

      var tspMsg = TspSolverOrchestrationPort.PrepareMessage();
      tspMsg["OrchestrationMessage"] = new EnumValue<OrchestrationMessage>(OrchestrationMessage.Prepare | OrchestrationMessage.ClearRuns | OrchestrationMessage.Start);
      var tpp = new TourProfitProblem {
        Tsp = (TravelingSalesmanProblem)TspParameter.Value.Clone(),
        Ksp = (BinaryKnapsackProblem)KspParameter.Value.Clone(),
        FixedKspSolution = bestKspSolution,
        Availability = AvailabilityParameter.Value.ToArray(),
        RentingRatio = RentingRatioParameter.Value.Value,
        MinSpeed = MinSpeedParameter.Value.Value,
        MaxSpeed = MaxSpeedParameter.Value.Value,
        DistanceType = distanceType
      };
      tpp.Encoding.Length = TspParameter.Value.Coordinates.Rows;
      tspMsg["Problem"] = tpp;
      TspSolverOrchestrationPort.SendMessage(tspMsg);
      cts.Token.ThrowIfCancellationRequested();

      var bestTspSolution = (Permutation)tspResults["Best TSP Solution"].Value.Clone();
      var coordinates = (DoubleMatrix)TspParameter.Value.Coordinates.Clone();
      var tour = new PathTSPTour(coordinates, bestTspSolution, new DoubleValue(TSPCoordinatesPathEvaluator.Apply(new TSPEuclideanPathEvaluator(), coordinates, bestTspSolution)));

      #region Analyze
      double objectiveValue = TtpUtils.Evaluate(TspParameter.Value, tour.Permutation.ToArray(), KspParameter.Value, loot.BinaryVector.ToArray(),
        AvailabilityParameter.Value.ToArray(), RentingRatioParameter.Value.Value, MinSpeedParameter.Value.Value, MaxSpeedParameter.Value.Value, distanceType);
      ((DoubleValue)message["Quality"]).Value = objectiveValue;

      IResult bestQuality;
      if (!Results.TryGetValue("Best TTP Quality", out bestQuality)) {
        Results.Add(new Result("Best TTP Quality", new DoubleValue(objectiveValue)));
        Results.Add(new Result("Best Tour", tour));
        Results.Add(new Result("Best Loot", loot));
      } else if (objectiveValue > ((DoubleValue)bestQuality.Value).Value) {
        ((DoubleValue)bestQuality.Value).Value = objectiveValue;
        Results["Best Tour"].Value = tour;
        Results["Best Loot"].Value = loot;
      }
      #endregion
    }
    #endregion
  }
}