static void Main(string[] args)
{
Instance Instance = new Instance(Path);
Weighting weighter = new Weighting(Instance);
// empty population --- Population size = boxes*containers --- Chromosome size = boxes
int PopulationSize = Instance.NumBoxes * Instance.NumContainers;
int ChromosomeSize = Instance.NumBoxes;
var Population = new Population();
for (var p = 0; p < PopulationSize; p++)
{
var Chromosome = new Chromosome();
WeightedBox[] WeightingArray = new WeightedBox[ChromosomeSize];
WeightingArray = weighter.InitilizeWeightingArray();
foreach (var box in WeightingArray)
{
Chromosome.Genes.Add(new Gene(box));
}
Chromosome.Genes.ShuffleFast();
double test = EvaluateFitness(Chromosome);
Population.Solutions.Add(Chromosome);
}
Console.ReadKey();
}
public static double EvaluateFitness(Chromosome chromosome)
{
Instance instance = new Instance(Path);
Weighting weighter = new Weighting(instance);
Dictionary <int, WeightedBox> InputToPack = new Dictionary <int, WeightedBox>();
int i = 0;
foreach (Gene Gene in chromosome.Genes)
{
//save Gene in WeightedBox instance
var box = new WeightedBox();
box = (WeightedBox)Gene.ObjectValue;
//update Weight and orientaton with absolute Ratio and dimension
box.Weight += weighter.Ratio[i];
int[] Dimension = new int[3];
for (int j = 0; j < 3; j++)
{
Dimension[j] = instance.Boxes[i, j + 1];
}
box.Orientation = ConvertDimension(box.Orientation, Dimension);
//Add to dictionary
InputToPack.Add(i, box);
i++;
}
InputToPack = InputToPack.OrderByDescending(x => x.Value.Weight).ToDictionary(x => x.Key, x => x.Value);
return(0);
}
public WeightedBox[] InitilizeWeightingArray()
{
WeightedBox[] WeightingArray = new WeightedBox[Instance.NumBoxes];
Random R = new Random();
double Range = BiasingStrength * MeanOfRatio;
for (int i = 0; i < Instance.NumBoxes; i++)
{
WeightingArray[i] = new WeightedBox();
WeightingArray[i].Weight = (R.NextDouble() * 2 * Range) - Range;
int[] s = new int[3] {
1, 2, 3
};
WeightingArray[i].Orientation = s.OrderBy(x => R.Next()).ToArray();
}
return(WeightingArray);
}
