source: branches/Templates/EmptyAlgorithm/EmptyAlgorithm/MyAlgorithm.cs @ 14364

using System;
using System.Threading;
using HeuristicLab.Common; // required for parameters collection
using HeuristicLab.Core; // required for parameters collection
using HeuristicLab.Data; // IntValue, ...
using HeuristicLab.Encodings.BinaryVectorEncoding;
using HeuristicLab.Optimization; // BasicAlgorithm
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.Binary;
using HeuristicLab.Random; // MersenneTwister

namespace EmptyAlgorithm {
  // each HL item needs to have a name and a description (BasicAlgorithm is an Item)
  // The name and description of items is shown in the GUI
  [Item(Name = "MyAlgorithm", Description = "An demo algorithm.")]

  // If the algorithm should be shown in the "New..." dialog it must be creatable. Entries in the new dialog are grouped to categories and ordered by priorities
  [Creatable(Category = CreatableAttribute.Categories.Algorithms, Priority = 999)]

  [StorableClass] // for persistence (storing your algorithm to a files or transfer to HeuristicLab.Hive
  public class MyAlgorithm : BasicAlgorithm {
    // This algorithm only works for BinaryProblems. 
    // Overriding the ProblemType property has the effect that only BinaryProblems can be set as problem
    // for the algorithm in the GUI
    public override Type ProblemType { get { return typeof(BinaryProblem); } }
    public new BinaryProblem Problem { get { return (BinaryProblem)base.Problem; } }

    #region parameters
    // If an algorithm has parameters then we usually also add properties to access these parameters.
    // This is not strictly required but considered good shape.
    private IFixedValueParameter<IntValue> MaxIterationsParameter {
      get { return (IFixedValueParameter<IntValue>)Parameters["MaxIterations"]; }
    }
    public int MaxIterations {
      get { return MaxIterationsParameter.Value.Value; }
      set { MaxIterationsParameter.Value.Value = value; }
    }
    #endregion

    // createable items must have a default ctor
    public MyAlgorithm() {
      // algorithm parameters are shown in the GUI
      Parameters.Add(new FixedValueParameter<IntValue>("MaxIterations", new IntValue(10000)));
    }

    // Persistence uses this ctor to improve deserialization efficiency.
    // If we would use the default ctor instead this would completely initialize the object (e.g. creating parameters)
    // even though the data is later overwritten by the stored data.
    [StorableConstructor]
    public MyAlgorithm(bool deserializing) : base(deserializing) { }

    // Each clonable item must have a cloning ctor (deep cloning, the cloner is used to handle cyclic object references)
    public MyAlgorithm(MyAlgorithm original, Cloner cloner) : base(original, cloner) {
      // Don't forget to call the cloning ctor of the base class
      // This class does not have fields, therefore we don't need to actually clone anything
    }

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

    protected override void Run(CancellationToken cancellationToken) {
      int maxIters = MaxIterations;
      var problem = Problem;
      var rand = new MersenneTwister(1234);

      var bestQuality = problem.Maximization ? double.MinValue : double.MaxValue;

      var curItersItem = new IntValue();
      var bestQualityItem = new DoubleValue(bestQuality);
      var curItersResult = new Result("Iteration", curItersItem);
      var bestQualityResult = new Result("Best quality", bestQualityItem);
      Results.Add(curItersResult);
      Results.Add(bestQualityResult);

      for (int i = 0; i < maxIters; i++) {
        curItersItem.Value = i;

        // -----------------------------
        // IMPLEMENT YOUR ALGORITHM HERE
        // -----------------------------


        // this is an example for random search
        // for a more elaborate algorithm check the source code of "HeuristicLab.Algorithms.ParameterlessPopulationPyramid"
        var cand = new BinaryVector(problem.Length, rand);
        var quality = problem.Evaluate(cand, rand); // calling Evaluate like this is not possible for all problems...
        if (problem.Maximization) bestQuality = Math.Max(bestQuality, quality);
        else bestQuality = Math.Min(quality, bestQuality);
        bestQualityItem.Value = bestQuality;

        // check the cancellation token to see if the used clicked "Stop"
        if (cancellationToken.IsCancellationRequested) break;
      }

      Results.Add(new Result("Execution time", new TimeSpanValue(this.ExecutionTime)));
    }
  }
}