//初始化聚类
private void earlistsCluster_modthree(Individual[] individuals, ref Individual[] population, int populationIndex)
{
//首先评估每个个体之间的MOD3指标
for (int i = 0; i < individuals.Length; i++)
{
FNModthreeEvaluation(ref individuals[i]);
FAModthreeEvaluation(ref individuals[i]);
}
//聚类，将个体聚类
Kmeans c = new Kmeans();
c.population = individuals;
c.gsmnum = gSectorNum;
c.ClusterMain(subPopSize, individuals.Length);
for (int i = 0; i < c.K; i++)
{
int curIndivIndex = populationIndex * subPopSize + i;
double minmodN = 0.0;
int index = 0;
for (int j = 0; j < c.classter[i].Count; j++)
{
if (j == 0)
{
minmodN = c.population[c.classter[i][j]].fitness_FNmod;
index = j;
}
else
{
if (c.population[c.classter[i][j]].fitness_FNmod < minmodN)
{
minmodN = c.population[c.classter[i][j]].fitness_FAmod3;
index = j;
}
}
}
for (int k = 0; k < population[i].gene.Length; k++)
{
population[curIndivIndex].gene[k] = c.population[c.classter[i][index]].gene[k];
}
}
}
private void earlistsCluster(Individual[] individuals, ref Individual[] population, int populationIndex)
{
//首先评估每个个体之间的MOD3指标
//for (int i = 0; i < individuals.Length; i++)
//{
// FNModthreeEvaluation(ref individuals[i]);
//}
//聚类，将个体聚类
Kmeans c = new Kmeans();
c.population = individuals;
c.gsmnum = gSectorNum;
c.ClusterMain(subPopSize, individuals.Length);
for (int i = 0; i < c.K; i++)
{
int curIndivIndex = populationIndex * subPopSize + i;
double minF = double.MaxValue;
int index = 0;
//index = rand.Next(c.classter[i].Count);
for (int j = 0; j < c.classter[i].Count; j++)
{
int curIndex = c.classter[i][j];
c.population[curIndex].totalFitness = 0;
for (int k = 0; k < objectNum; k++)
c.population[curIndex].totalFitness += weightMatrix[populationIndex, k] * c.population[curIndex].fitnessVec[k] / c.population[curIndex].fitnessVecOri[k];
if (c.population[curIndex].totalFitness < minF)
{
minF = c.population[curIndex].totalFitness;
index = j;
}
}
for (int k = 0; k < population[i].gene.Length; k++)
population[curIndivIndex].gene[k] = c.population[c.classter[i][index]].gene[k];
}
}
