public override int[] NewTemporaryIndividual(double[,] sgm, int n)
{
Random rand = new Random(Guid.NewGuid().GetHashCode());
int[] seq = new int[n];
var msgm = GeneticAlgorithmFunctions.CopyArray(sgm);
seq[0] = 1;
// Go through sgm and build new sequence
for (int i = 1; i < seq.Length; i++)
{
int from = seq[i - 1];
// Remove the possibility of going back to the FROM node
var next = Markov.Markov.GetNext(from, msgm);
seq[i] = next;
Markov.Markov.RemoveColumn(from, msgm);
}
if (seq.Distinct().Count() != seq.Length || !GeneticAlgorithmFunctions.IsDistinct(seq))
{
Console.Error.WriteLine("Error with sequence");
Console.WriteLine(String.Join(", ", seq));
System.Environment.Exit(1);
}
return(seq);
}
/// <summary>
/// Intensification method according to Zhang et al. (2018) in Algorithm 3.
/// Some parts have been tweaked to adapt to ordered problem constraints
/// </summary>
/// <param name="pop"></param>
/// <param name="subPopSize"></param>
/// <returns></returns>
public override Genotype[] Intensification(Genotype[] pop, int subPopSize, double[,] sgm)
{
Random rand = new Random(Guid.NewGuid().GetHashCode());
//----------------------------------------------------------------------------------------------------------
// Step 1: Compute S_E and copy the best individuals into the next generation
//----------------------------------------------------------------------------------------------------------
Genotype[] subPop = GetElite(subPopSize, pop, M_d);
Genotype[] newPop = new Genotype[subPop.Length];
var sgm = BuildSgm(subPop);
var S_E = GetReferencePoint(sgm);
//----------------------------------------------------------------------------------------------------------
// Step 2: According to S_E, compute each vector of ppSGM M_P
//----------------------------------------------------------------------------------------------------------
// Get the control amplitude
double CA = CalcControlAmplitude(pop);
var M_P = BuildPerturbationMatrix(sgm, CA, false);
//----------------------------------------------------------------------------------------------------------
// Step 3: Breed temporary individuals by using pp=SGM and M_P and Alg 1. Similar to Alg 3, execute
// crossover between temporary individuals and intensification subpopulation after updating.
//----------------------------------------------------------------------------------------------------------
// Calc probabilty of mutation
double p_m = 1 / (double)this.N;
// Create the temporary individuals that reflect the subpopulation
int[][] temporaryIndividuals = new int[pop.Length][];
Parallel.For(0, subPop.Length, ind => {
temporaryIndividuals[ind] = this.NewTemporaryIndividual(M_P, this.N);
});
// Crossover and mutation
Parallel.For(0, subPop.Length, i => {
if (i == 0)
{
newPop[i] = subPop[i];
}
// var candidate = temporaryIndividuals[i];
var candidate = pop[GeneticAlgorithmFunctions.Select(pop.Length)].Sequence;
var genotype = subPop[i];
var seq = GeneticAlgorithm.GeneticAlgorithmFunctions.Crossover(candidate, genotype.Sequence);
seq = GeneticAlgorithm.GeneticAlgorithmFunctions.Mutate(seq, p_m);
var cost = Problem.CalcCostRoute(seq);
newPop[i] = new Genotype(seq, cost);
});
// Sort and return
newPop = subPop.OrderBy(c => c.Cost).ToArray();
return(newPop);
}
