public void CapacityZeroTest()
{
List <int> solution;
// 0 capacity, 0 items => 0
solution = KnapsackProblem.Solve(0, new List <Item>());
Assert.AreEqual(solution.Count, 0);
// 0 capacity, 1 item => 0
List <Item> items = new List <Item>
{
new Item(5, 5)
};
solution = KnapsackProblem.Solve(0, items);
Assert.AreEqual(solution.Count, 0);
// 0 capacity, bunch of items => 0
items = new List <Item>();
for (int i = 0; i < 100; i++)
{
items.Add(new Item(this._Rand.Next(), this._Rand.Next()));
}
solution = KnapsackProblem.Solve(0, items);
Assert.AreEqual(solution.Count, 0);
}
private async void calculateMatrixDynamic(object sender, RoutedEventArgs e)
{
try
{
Matrix matrix = getMatrixFromGrid(gridTextBox);
int capacity = int.Parse(capacityTextBox.Text);
Matrix[] pq = KnapsackProblem.DynamicMethod(matrix, capacity);
int[] resultTable = KnapsackProblem.ShowItems(matrix, pq[1]);
resultDynamicTextBlock.Text = "Przedmioty do zabrania: " + string.Join(",", resultTable.Select(i => i.ToString()));
resultDynamicSumTextBlock.Text = "Wartość Plecaka: " + pq[0].getMatrix()[pq[0].getMatrix().GetLength(0) - 1, pq[0].getMatrix().GetLength(1) - 1].ToString();
resultGridDynamic.Visibility = Visibility.Visible;
resultGridDynamic.Opacity = 0;
resultGridDynamic.OpacityTransition = new ScalarTransition()
{
Duration = new TimeSpan(0, 0, 0, 0, 500)
};
resultGridDynamic.Opacity = 1;
}
catch (Exception exp)
{
ContentDialog dialog = new ContentDialog {
Title = "Error", Content = exp.Message, CloseButtonText = "ok"
};
ContentDialogResult result = await dialog.ShowAsync();
}
}
public void CombineLessValuableTest()
{
List <int> solution;
// Most valuable item should be the only one included
List <Item> items = new List <Item>
{
new Item(55, 55),
new Item(50, 45),
new Item(50, 45)
};
solution = KnapsackProblem.Solve(100, items);
Assert.AreEqual(solution.Count, 2);
Assert.IsTrue(solution.Contains(1));
Assert.IsTrue(solution.Contains(2));
}
public void ResolveTest()
{
var maxWeight = 6;
var result = KnapsackProblem.Resolve(CreateThings(), maxWeight);
var sumWeight = result.Sum(thing => thing.Weight);
var sumValue = result.Sum(thing => thing.Value);
Assert.IsTrue(sumWeight <= maxWeight);
Console.WriteLine($"SumValue:{sumValue}, SumWeight:{sumWeight}, MaxWeight:{maxWeight}");
Console.WriteLine("Result[] = {");
foreach (var thing in result)
{
Console.WriteLine($" Value:{thing.Value}, Weight:{thing.Weight}");
}
Console.WriteLine("}");
}
private LocalSearch CreateLocalSearchKnapsackSample()
{
LocalSearch ls = new LocalSearch();
#region Problem Configuration
KnapsackProblem problem = new KnapsackProblem();
problem.BestKnownQuality = new DoubleValue(362);
problem.BestKnownSolution = new HeuristicLab.Encodings.BinaryVectorEncoding.BinaryVector(new bool[] {
true, false, false, true, true, true, true, true, false, true, true, true, true, true, true, false, true, false, true, true, false, true, true, false, true, false, true, true, true, false, true, true, false, true, true, false, true, false, true, true, true, true, true, true, true, true, true, true, true, true, true, false, true, false, false, true, true, false, true, true, true, true, true, true, true, true, false, true, false, true, true, true, true, false, true, true, true, true, true, true, true, true
});
problem.EvaluatorParameter.Value = new KnapsackEvaluator();
problem.SolutionCreatorParameter.Value = new RandomBinaryVectorCreator();
problem.KnapsackCapacity.Value = 297;
problem.Maximization.Value = true;
problem.Penalty.Value = 1;
problem.Values = new IntArray(new int[] {
6, 1, 1, 6, 7, 8, 7, 4, 2, 5, 2, 6, 7, 8, 7, 1, 7, 1, 9, 4, 2, 6, 5, 3, 5, 3, 3, 6, 5, 2, 4, 9, 4, 5, 7, 1, 4, 3, 5, 5, 8, 3, 6, 7, 3, 9, 7, 7, 5, 5, 7, 1, 4, 4, 3, 9, 5, 1, 6, 2, 2, 6, 1, 6, 5, 4, 4, 7, 1, 8, 9, 9, 7, 4, 3, 8, 7, 5, 7, 4, 4, 5
});
problem.Weights = new IntArray(new int[] {
1, 9, 3, 6, 5, 3, 8, 1, 7, 4, 2, 1, 2, 7, 9, 9, 8, 4, 9, 2, 4, 8, 3, 7, 5, 7, 5, 5, 1, 9, 8, 7, 8, 9, 1, 3, 3, 8, 8, 5, 1, 2, 4, 3, 6, 9, 4, 4, 9, 7, 4, 5, 1, 9, 7, 6, 7, 4, 7, 1, 2, 1, 2, 9, 8, 6, 8, 4, 7, 6, 7, 5, 3, 9, 4, 7, 4, 6, 1, 2, 5, 4
});
problem.Name = "Knapsack Problem";
problem.Description = "Represents a Knapsack problem.";
#endregion
#region Algorithm Configuration
ls.Name = "Local Search - Knapsack";
ls.Description = "A local search algorithm that solves a randomly generated Knapsack problem";
ls.Problem = problem;
ls.MaximumIterations.Value = 1000;
ls.MoveEvaluator = ls.MoveEvaluatorParameter.ValidValues
.OfType <KnapsackOneBitflipMoveEvaluator>()
.Single();
ls.MoveGenerator = ls.MoveGeneratorParameter.ValidValues
.OfType <ExhaustiveOneBitflipMoveGenerator>()
.Single();
ls.MoveMaker = ls.MoveMakerParameter.ValidValues
.OfType <OneBitflipMoveMaker>()
.Single();
ls.SampleSize.Value = 100;
ls.Seed.Value = 0;
ls.SetSeedRandomly.Value = true;
#endregion
ls.Engine = new ParallelEngine.ParallelEngine();
return(ls);
}
private async void calculateMatrixGreedy(object sender, RoutedEventArgs e)
{
try
{
Matrix matrix = getMatrixFromGrid(gridTextBox);
int capacity = int.Parse(capacityTextBox.Text);
if (KnapsackProblem.CheckWeights(matrix, capacity) == 0)
{
ContentDialog dialog = new ContentDialog {
Title = "Information", Content = "Nic się nie zmieści do plecaka", CloseButtonText = "Ok"
};
ContentDialogResult result = await dialog.ShowAsync();
}
else
{
double[] quot = KnapsackProblem.CountQuotient(matrix);
int value = 0;
Matrix newMatrix = KnapsackProblem.GreedyMethod(matrix, capacity, quot, ref value);
resultGreedySumTextBlock.Text = "Wartość Plecaka: " + value.ToString();
resultGreedyTextBlock.Text = "Przedmioty do zabrania: ";
for (int i = 0; i < newMatrix.getMatrix().GetLength(1); i++)
{
resultGreedyTextBlock.Text += " Przedmiot ";
resultGreedyTextBlock.Text += newMatrix.getMatrix()[1, i].ToString();
resultGreedyTextBlock.Text += " x ";
resultGreedyTextBlock.Text += newMatrix.getMatrix()[0, i].ToString();
resultGreedyTextBlock.Text += " ; ";
}
resultGridGreedy.Visibility = Visibility.Visible;
resultGridGreedy.Opacity = 0;
resultGridGreedy.OpacityTransition = new ScalarTransition()
{
Duration = new TimeSpan(0, 0, 0, 0, 500)
};
resultGridGreedy.Opacity = 1;
}
}
catch (Exception exp)
{
ContentDialog dialog = new ContentDialog {
Title = "Error", Content = exp.Message, CloseButtonText = "ok"
};
ContentDialogResult result = await dialog.ShowAsync();
}
}
public void SearchTest()
{
var capacity = 14;
var items = new KnapsackItem[]
{
new KnapsackItem(weight: 1, value: 13),
new KnapsackItem(weight: 6, value: 5),
new KnapsackItem(weight: 6, value: 4),
new KnapsackItem(weight: 5, value: 2)
};
var result = KnapsackProblem.Search(items, capacity);
var backTrack = KnapsackProblem.GetBackTrack(result, items, capacity, items.Length);
const int expectedSumValue = 22;
Assert.IsTrue(backTrack.Sum(i => i.Value) == expectedSumValue, "Incorrectly found the sum of the values");
}
public void FindsSolutionTest()
{
for (int i = 0; i < 10; i++)
{
List <Item> items = new List <Item>();
for (int j = 0; j < 1000; j++)
{
Item item = new Item(this._Rand.Next(1, 1000), this._Rand.Next());
items.Add(item);
}
List <int> weights = items.Select(item => item.Weight).ToList();
int capacity = this._Rand.Next(weights.Min(), weights.Max());
List <int> solution = KnapsackProblem.Solve(capacity, items);
int totalUsedCapacity = solution.Sum(s => weights[s]);
Assert.IsTrue(totalUsedCapacity <= capacity);
}
}
protected void Run()
{
Console.Out.WriteLine("Métaheuristiques d'optimisation\n");
IProblem kspb = new KnapsackProblem();
RunAlgorithms(kspb);
Console.Out.WriteLine("*****************************************************************\n");
IProblem kspbRandom = new KnapsackProblem(100, 30, 20);
RunAlgorithms(kspbRandom);
Console.Out.WriteLine("FIN");
while (true)
{
;
}
}
private LocalSearch CreateLocalSearchKnapsackSample() {
LocalSearch ls = new LocalSearch();
#region Problem Configuration
KnapsackProblem problem = new KnapsackProblem();
problem.BestKnownQuality = new DoubleValue(362);
problem.BestKnownSolution = new HeuristicLab.Encodings.BinaryVectorEncoding.BinaryVector(new bool[] {
true , false, false, true , true , true , true , true , false, true , true , true , true , true , true , false, true , false, true , true , false, true , true , false, true , false, true , true , true , false, true , true , false, true , true , false, true , false, true , true , true , true , true , true , true , true , true , true , true , true , true , false, true , false, false, true , true , false, true , true , true , true , true , true , true , true , false, true , false, true , true , true , true , false, true , true , true , true , true , true , true , true});
problem.EvaluatorParameter.Value = new KnapsackEvaluator();
problem.SolutionCreatorParameter.Value = new RandomBinaryVectorCreator();
problem.KnapsackCapacity.Value = 297;
problem.Maximization.Value = true;
problem.Penalty.Value = 1;
problem.Values = new IntArray(new int[] {
6, 1, 1, 6, 7, 8, 7, 4, 2, 5, 2, 6, 7, 8, 7, 1, 7, 1, 9, 4, 2, 6, 5, 3, 5, 3, 3, 6, 5, 2, 4, 9, 4, 5, 7, 1, 4, 3, 5, 5, 8, 3, 6, 7, 3, 9, 7, 7, 5, 5, 7, 1, 4, 4, 3, 9, 5, 1, 6, 2, 2, 6, 1, 6, 5, 4, 4, 7, 1, 8, 9, 9, 7, 4, 3, 8, 7, 5, 7, 4, 4, 5});
problem.Weights = new IntArray(new int[] {
1, 9, 3, 6, 5, 3, 8, 1, 7, 4, 2, 1, 2, 7, 9, 9, 8, 4, 9, 2, 4, 8, 3, 7, 5, 7, 5, 5, 1, 9, 8, 7, 8, 9, 1, 3, 3, 8, 8, 5, 1, 2, 4, 3, 6, 9, 4, 4, 9, 7, 4, 5, 1, 9, 7, 6, 7, 4, 7, 1, 2, 1, 2, 9, 8, 6, 8, 4, 7, 6, 7, 5, 3, 9, 4, 7, 4, 6, 1, 2, 5, 4});
problem.Name = "Knapsack Problem";
problem.Description = "Represents a Knapsack problem.";
#endregion
#region Algorithm Configuration
ls.Name = "Local Search - Knapsack";
ls.Description = "A local search algorithm that solves a randomly generated Knapsack problem";
ls.Problem = problem;
ls.MaximumIterations.Value = 1000;
ls.MoveEvaluator = ls.MoveEvaluatorParameter.ValidValues
.OfType<KnapsackOneBitflipMoveEvaluator>()
.Single();
ls.MoveGenerator = ls.MoveGeneratorParameter.ValidValues
.OfType<ExhaustiveOneBitflipMoveGenerator>()
.Single();
ls.MoveMaker = ls.MoveMakerParameter.ValidValues
.OfType<OneBitflipMoveMaker>()
.Single();
ls.SampleSize.Value = 100;
ls.Seed.Value = 0;
ls.SetSeedRandomly.Value = true;
#endregion
ls.Engine = new ParallelEngine.ParallelEngine();
return ls;
}
public void GreedyTest()
{
List <int> solution;
// Most valuable item should be the only one included
List <Item> items = new List <Item>
{
new Item(1, 500),
new Item(1, 300),
new Item(2, 500)
};
solution = KnapsackProblem.Solve(1, items);
Assert.AreEqual(solution.Count, 1);
Assert.IsTrue(solution.Contains(0));
items.Add(new Item(1, 500));
solution = KnapsackProblem.Solve(2, items);
Assert.AreEqual(solution.Count, 2);
Assert.IsTrue(solution.Contains(0));
Assert.IsTrue(solution.Contains(3));
}
public KnapsackOptimizedSolver(KnapsackProblem problem)
{
_problem = problem;
}
public KnapsackIterativeSolver(KnapsackProblem problem)
{
_problem = problem;
}
