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source: branches/ichiriac/HeuristicLab.Algorithms.DifferentialEvolution/DifferentialEvolution.cs @ 13619

Last change on this file since 13619 was 13619, checked in by ichiriac, 7 years ago

Create basic structure for Differential Evolution Plugin

Created basic structure for the plugin and added all needed references.

File size: 6.9 KB
Line 
1using HeuristicLab.Analysis;
2using HeuristicLab.Common;
3using HeuristicLab.Core;
4using HeuristicLab.Data;
5using HeuristicLab.Encodings.RealVectorEncoding;
6using HeuristicLab.Optimization;
7using HeuristicLab.Parameters;
8using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
9using HeuristicLab.Problems.TestFunctions;
10using HeuristicLab.Random;
11using System;
12using System.Linq;
13using System.Threading;
14
15namespace HeuristicLab.Algorithms.DifferentialEvolution
16{
17
18    [Item("Differential Evolution (DE)", "")]
19    [StorableClass]
20    [Creatable(CreatableAttribute.Categories.PopulationBasedAlgorithms, Priority = 400)]
21    public class DifferentialEvolution : BasicAlgorithm
22    {
23
24        public override Type ProblemType
25        {
26            get { return typeof(SingleObjectiveTestFunctionProblem); }
27        }
28        public new SingleObjectiveTestFunctionProblem Problem
29        {
30            get { return (SingleObjectiveTestFunctionProblem)base.Problem; }
31            set { base.Problem = value; }
32        }
33
34        private readonly IRandom random = new MersenneTwister();
35     
36        #region ParameterNames
37        private const string MaximumEvaluationsParameterName = "Maximum Evaluations";
38        private const string SeedParameterName = "Seed";
39        private const string SetSeedRandomlyParameterName = "SetSeedRandomly";
40        #endregion
41
42        #region ParameterProperties
43        public IFixedValueParameter<IntValue> MaximumEvaluationsParameter
44        {
45            get { return (IFixedValueParameter<IntValue>)Parameters[MaximumEvaluationsParameterName]; }
46        }
47        public IFixedValueParameter<IntValue> SeedParameter
48        {
49            get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
50        }
51        public FixedValueParameter<BoolValue> SetSeedRandomlyParameter
52        {
53            get { return (FixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
54        }
55        #endregion
56
57        #region Properties
58        public int MaximumEvaluations
59        {
60            get { return MaximumEvaluationsParameter.Value.Value; }
61            set { MaximumEvaluationsParameter.Value.Value = value; }
62        }
63        public int Seed
64        {
65            get { return SeedParameter.Value.Value; }
66            set { SeedParameter.Value.Value = value; }
67        }
68        public bool SetSeedRandomly
69        {
70            get { return SetSeedRandomlyParameter.Value.Value; }
71            set { SetSeedRandomlyParameter.Value.Value = value; }
72        }
73        #endregion
74
75        #region ResultsProperties
76        private double ResultsBestQuality
77        {
78            get { return ((DoubleValue)Results["Best Quality"].Value).Value; }
79            set { ((DoubleValue)Results["Best Quality"].Value).Value = value; }
80        }
81
82        private RealVector ResultsBestSolution
83        {
84            get { return (RealVector)Results["Best Solution"].Value; }
85            set { Results["Best Solution"].Value = value; }
86        }
87
88       
89        private int ResultsEvaluations
90        {
91            get { return ((IntValue)Results["Evaluations"].Value).Value; }
92            set { ((IntValue)Results["Evaluations"].Value).Value = value; }
93        }
94        private int ResultsIterations
95        {
96            get { return ((IntValue)Results["Iterations"].Value).Value; }
97            set { ((IntValue)Results["Iterations"].Value).Value = value; }
98        }
99
100        private DataTable ResultsQualities
101        {
102            get { return ((DataTable)Results["Qualities"].Value); }
103        }
104        private DataRow ResultsQualitiesBest
105        {
106            get { return ResultsQualities.Rows["Best Quality"]; }
107        }
108
109        #endregion
110
111        [StorableConstructor]
112        protected DifferentialEvolution(bool deserializing) : base(deserializing) { }
113
114        protected DifferentialEvolution(DifferentialEvolution original, Cloner cloner)
115          : base(original, cloner)
116        {
117        }
118
119        public override IDeepCloneable Clone(Cloner cloner)
120        {
121            return new DifferentialEvolution(this, cloner);
122        }
123
124        public DifferentialEvolution()
125        {
126            Parameters.Add(new FixedValueParameter<IntValue>(MaximumEvaluationsParameterName, "", new IntValue(Int32.MaxValue)));
127            Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
128            Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
129        }
130
131
132
133        protected override void Run(CancellationToken cancellationToken)
134        {
135     
136            // Set up the algorithm
137            if (SetSeedRandomly) Seed = new System.Random().Next();
138            random.Reset(Seed);
139
140            // Set up the results display
141            Results.Add(new Result("Iterations", new IntValue(0)));
142            Results.Add(new Result("Evaluations", new IntValue(0)));
143            Results.Add(new Result("Best Solution", new RealVector()));
144            Results.Add(new Result("Best Quality", new DoubleValue(double.NaN)));
145            var table = new DataTable("Qualities");
146            table.Rows.Add(new DataRow("Best Quality"));
147            Results.Add(new Result("Qualities", table));
148
149            var dim = Problem.ProblemSize.Value;
150            var lb = Problem.Bounds[0, 0];
151            var ub = Problem.Bounds[0, 1];
152            var range = ub - lb;
153            var randomEnumerable = Enumerable.Range(0, dim).Select(_ => random.NextDouble()*range + lb);
154            int evals = 0;
155            double bestSolutionQuality = double.PositiveInfinity;
156            RealVector cand = new RealVector(randomEnumerable.ToArray());
157            RealVector bestSolution = cand;
158            // Loop until iteration limit reached or canceled.
159            while (evals < MaximumEvaluations  && !cancellationToken.IsCancellationRequested)
160            {
161                cand = new RealVector(randomEnumerable.ToArray());
162                var q = Obj(cand);
163                evals++;
164                if (q < bestSolutionQuality) {
165                    bestSolutionQuality = q;
166                    bestSolution = cand;
167                }
168                // cancellationToken.ThrowIfCancellationRequested();
169                if(evals % 1000 == 0) ResultsQualitiesBest.Values.Add(bestSolutionQuality);
170            }
171            ResultsEvaluations = evals;
172            ResultsBestSolution = bestSolution;
173            ResultsBestQuality = bestSolutionQuality;
174         }
175       
176        public double Obj(RealVector x)
177        {
178            if(Problem.Maximization.Value)
179            return -Problem.Evaluator.Evaluate(x);
180
181            return Problem.Evaluator.Evaluate(x);
182        }
183    }
184}
185
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