public double[,] edge_Clustered_Tree_Mutation(double[,] par, int num_Vertex, double mutation_Rate, int num_Cluster, int[][] vertex_In_Cluster, Random rnd)
{
Initialize_Chromosome init_Chrome = new Initialize_Chromosome();
double[,] child = new double[num_Vertex, num_Vertex];
for (int i = 0; i < num_Vertex; i++)
{
for (int j = 0; j < num_Vertex; j++)
{
child[i, j] = par[i, j];
}
}
for (int ii = 0; ii < num_Vertex; ii++)
{
int idx_Cluster = rnd.Next(num_Cluster);
while (vertex_In_Cluster[idx_Cluster].Length < 3)
{
idx_Cluster = rnd.Next(num_Cluster);
}
//01. Chuyển ma trận của cluster thành ma trận cây để áp dụng đột biến
double[,] weight_Cluster_Matrix;
double[,] spanning_Tree_of_Cluster;
int num_Vertex_in_Cluster = vertex_In_Cluster[idx_Cluster].Length;
weight_Cluster_Matrix = init_Chrome.create_Weight_Matrix_for_Cluster(num_Vertex, num_Vertex_in_Cluster, par, vertex_In_Cluster[idx_Cluster]);
spanning_Tree_of_Cluster = edge_Mutation(weight_Cluster_Matrix, num_Vertex_in_Cluster, mutation_Rate, rnd);
//Chuyen ra cay khung cua do thi G
int[] cluster = new int[num_Vertex_in_Cluster];
for (int i = 0; i < num_Vertex_in_Cluster; i++)
{
cluster[i] = vertex_In_Cluster[idx_Cluster][i];
}
for (int k = 0; k < num_Vertex_in_Cluster; k++)
{
for (int j = 0; j < num_Vertex_in_Cluster; j++)
{
//child[cluster[k], cluster[j]] = 0;
child[cluster[k], cluster[j]] = spanning_Tree_of_Cluster[k, j];
}
}
}
return(child);
}
/*********************************************************************************************************************************************
* Tìm ma trận liên kết các cluster từ cây khung
* + Hướng 1: Chỉ tìm liên kết không quan tâm tới trọng số
* + Hướng 2: Tìm liên kết có lưu trọng số, và các cạnh nối giữa các cluster để thực hiện lai ghép về sau <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
**********************************************************************************************************************************************/
private double[,] primRST_Cluster_Crossover(double[,] par_1, double[,] par_2, int num_Vertex, int num_Cluster, int[][] vertex_In_Cluster, Random rnd)
{
double[,] G_cr = new double[num_Vertex, num_Vertex];
double[,] T = new double[num_Vertex, num_Vertex];
Initialize_Chromosome init_Chrome = new Initialize_Chromosome();
//Khoi tao
for (int i = 0; i < num_Vertex; i++)
{
for (int j = i; j < num_Vertex; j++)
{
T[i, j] = 0;
T[j, i] = 0;
}
}
//Ap dung PrimRST crossover cho tung cluster
double[,] weight_Cluster_Matrix_Par_1, weight_Cluster_Matrix_Par_2;
double[,] spanning_Tree_of_Cluster;
int num_Vertex_in_Cluster = 0;
for (int i = 0; i < num_Cluster; i++)
{
num_Vertex_in_Cluster = vertex_In_Cluster[i].Length;
weight_Cluster_Matrix_Par_1 = init_Chrome.create_Weight_Matrix_for_Cluster(num_Vertex, num_Vertex_in_Cluster, par_1, vertex_In_Cluster[i]);
weight_Cluster_Matrix_Par_2 = init_Chrome.create_Weight_Matrix_for_Cluster(num_Vertex, num_Vertex_in_Cluster, par_2, vertex_In_Cluster[i]);
spanning_Tree_of_Cluster = primRST_Crossover(weight_Cluster_Matrix_Par_1, weight_Cluster_Matrix_Par_2, num_Vertex_in_Cluster, rnd);
//Chuyen ra cay khung cua do thi G
for (int k = 0; k < num_Vertex_in_Cluster; k++)
{
for (int j = 0; j < num_Vertex_in_Cluster; j++)
{
if (spanning_Tree_of_Cluster[k, j] > 0)
{
T[vertex_In_Cluster[i][k], vertex_In_Cluster[i][j]] = spanning_Tree_of_Cluster[k, j];
}
}
}
}
//Tạo cây khung cho đại diện các nhóm
//01. Tim cạnh liên kết các nhóm của cha mẹ => các cạnh của cha mẹ có thể liên kết tới các đỉnh khác nhau của cùng 1 nhóm =>
weight_Cluster_Matrix_Par_1 = find_Spanning_Tree_Between_Clusters(par_1, num_Vertex, num_Cluster, vertex_In_Cluster);
weight_Cluster_Matrix_Par_2 = find_Spanning_Tree_Between_Clusters(par_2, num_Vertex, num_Cluster, vertex_In_Cluster);
//02. Tạo các thể con cho các cạnh nay
spanning_Tree_of_Cluster = primRST_Crossover(weight_Cluster_Matrix_Par_1, weight_Cluster_Matrix_Par_2, num_Cluster, rnd);
//Chuyen ra cay khung cua do thi G
int[] idx_Cluster = new int[num_Cluster];
for (int i = 0; i < num_Cluster; i++)
{
int vt = rnd.Next(vertex_In_Cluster[i].Length);
idx_Cluster[i] = vertex_In_Cluster[i][vt];
}
for (int k = 0; k < num_Cluster; k++)
{
for (int j = 0; j < num_Cluster; j++)
{
if (spanning_Tree_of_Cluster[k, j] > 0)
{
T[idx_Cluster[k], idx_Cluster[j]] = spanning_Tree_of_Cluster[k, j];
}
}
}
return(T);
}
/*********************************************************************************************************************************************
* G. R. Raidl and B. A. Julstrom, “Edge sets: an effective evolutionary coding of spanning trees,” IEEE Transactions on evolutionary computation, vol. 7, no. 3, pp. 225–239, 2003.
* Lai ghép cho biểu diễn cây sử dụng thuậ toán PrimRST để tạo cây
*
********************************************************************************************************************************************/
private double[,] primRST_Crossover(double[,] par1, double[,] par2, int num_Vertex, Random rnd)
{
double[,] G_cr = new double[num_Vertex, num_Vertex];
int[,] aaa = new int[num_Vertex, num_Vertex];
Initialize_Chromosome init_Chrome = new Initialize_Chromosome();
//01. Tao do thi Gcr
for (int i = 0; i < num_Vertex; i++)
{
for (int j = 0; j < num_Vertex; j++)
{
G_cr[i, j] = par1[i, j] + par2[i, j];
}
}
ReadWriteFile ioFile = new ReadWriteFile();
//for (int i = 0; i < num_Vertex; i++)
//{
// for (int j = 0; j < num_Vertex; j++)
// {
// aaa[i, j] = (int)par1[i, j];
// }
//}
//ioFile.draw_Plot_in_Matlab("par_1" + @".m", num_Vertex, aaa);
//for (int i = 0; i < num_Vertex; i++)
//{
// for (int j = 0; j < num_Vertex; j++)
// {
// aaa[i, j] = (int)par2[i, j];
// }
//}
//ioFile.draw_Plot_in_Matlab("par_2" + @".m", num_Vertex, aaa);
//for (int i = 0; i < num_Vertex; i++)
//{
// for (int j = 0; j < num_Vertex; j++)
// {
// aaa[i, j] = (int)par1[i, j] + (int)par2[i, j];
// }
//}
//ioFile.draw_Plot_in_Matlab("aaa" + @".m", num_Vertex, aaa);
//02. Ap dung PrimRST de tao ca the con
double[,] aaaaa = init_Chrome.primRST(num_Vertex, G_cr, rnd);
//if (aaaaa == null)
//{
// //ReadWriteFile ioFile = new ReadWriteFile();
// ioFile.writeMatrixToFile("G_cr.txt", G_cr, num_Vertex, false);
// Console.WriteLine("null child");
// for (int i = 0; i < num_Vertex; i++)
// {
// for (int j = 0; j < num_Vertex; j++)
// {
// aaa[i, j] = (int)par1[i, j];
// }
// }
// ioFile.draw_Plot_in_Matlab("par_1" + @".m", num_Vertex, aaa);
// for (int i = 0; i < num_Vertex; i++)
// {
// for (int j = 0; j < num_Vertex; j++)
// {
// aaa[i, j] = (int)par2[i, j];
// }
// }
// ioFile.draw_Plot_in_Matlab("par_2" + @".m", num_Vertex, aaa);
// for (int i = 0; i < num_Vertex; i++)
// {
// for (int j = 0; j < num_Vertex; j++)
// {
// aaa[i, j] = (int)par1[i, j] + (int)par2[i, j];
// }
// }
// ioFile.draw_Plot_in_Matlab("aaa" + @".m", num_Vertex, aaa);
// Console.ReadKey();
//}
//for (int i = 0; i < num_Vertex; i++)
//{
// for (int j = 0; j < num_Vertex; j++)
// {
// aaa[i, j] = (int)aaaaa[i, j];
// }
//}
//ioFile.draw_Plot_in_Matlab("child" + @".m", num_Vertex, aaa);
return(aaaaa);
}
public Data_MFEA main_GA_1(string init_Individual_Alg, TerminationCondition termination_Condition, int pop_Size, Tasks[] tsk, double lambda,
Crossover_Parameters cros_Para, Mutation_Parameters mu_Para, string selection_pressure, string local_Search_Method, string population_Diversity_Method, Random rnd)
{
Initialize_Chromosome init_Chromosome = new Initialize_Chromosome();
double best_Fitness_In_Generation = double.MaxValue, //Cá thể tốt nhất của mỗi TAS
diversity_of_Pop = 0.0f;
List <double> ev_Best_Fitness = new List <double>(); //Lưu best fitness tốt nhất qua các thế hệ
List <double> diversity_of_pop_in_Gers = new List <double>(); //Lưu độ đa dạng quần thể qua các thế hệ
Chromosome best_Ind_data;
Evaluate evaluate = new Evaluate();
Chromosome[] current_pop = new Chromosome[pop_Size];
Chromosome[] offspring_pop = new Chromosome[pop_Size];
Chromosome[] c_pop = new Chromosome[2 * pop_Size];
int num_Genens = tsk[0].Dims, //D_multitask
count_Evaluate = 0, //Biến trung gian đếm số lần đánh giá khi gọi hàm evaluate
bst_Idx;
best_Ind_data = new Chromosome(num_Genens, 1);
for (int i = 0; i < pop_Size; i++)
{
current_pop[i] = new Chromosome(num_Genens, 1);
offspring_pop[i] = new Chromosome(num_Genens, 1);
c_pop[i] = new Chromosome(num_Genens, 1);
c_pop[i + pop_Size] = new Chromosome(num_Genens, 1);
}
//Initial Population
for (int i = 0; i < pop_Size; i++)
{
init_Chromosome.initialize_Chromosome(init_Individual_Alg, num_Genens, tsk[0], rnd, ref current_pop[i]);
current_pop[i].Skill_Factor = -1;
current_pop[i].Constraint_violation[0] = 0;//DO CHI CO 1 TASSK
}
number_of_evaluations = 0;
//Evaluate population
bst_Idx = -1;
for (int i = 0; i < pop_Size; i++)
{
evaluate.evaluate(tsk, 1, lambda, ref current_pop[i], ref count_Evaluate);
number_of_evaluations = number_of_evaluations + count_Evaluate;
if (current_pop[i].Factorial_Cost[0] < best_Fitness_In_Generation)
{
best_Fitness_In_Generation = current_pop[i].Factorial_Cost[0];//do chi co 1 task
bst_Idx = i;
}
}
mfea_Class.copy_Individual(current_pop[bst_Idx], ref best_Ind_data, num_Genens, 1);
ev_Best_Fitness.Add(best_Fitness_In_Generation);
diversity_of_Pop = evaluate.compute_Population_Diversity(current_pop, pop_Size, num_Genens, population_Diversity_Method);
diversity_of_pop_in_Gers.Add(diversity_of_Pop);
int generation = 1;
termination_Condition.Number_of_evaluations = number_of_evaluations;
//while (generation < max_Generations)
while (termination_Condition.checkTerminationCondition())
{
//i. Apply genetic operators on current-pop to generate an offspring-pop (C). Refer to Algorithm 2.
int count_Child = 0;
while (count_Child < pop_Size)
{
int pos1 = rnd.Next(pop_Size);
int pos2 = rnd.Next(pop_Size);
while (pos1 == pos2)
{
pos2 = rnd.Next(pop_Size);
}
List <Chromosome> lst_Child = new List <Chromosome>();
mfea_Class.apply_Genetic_Operators(current_pop[pos1], current_pop[pos2], num_Genens, 1, cros_Para, mu_Para, ref lst_Child, tsk[0], rnd);
for (int i = 0; i < lst_Child.Count; i++)
{
mfea_Class.copy_Individual(lst_Child[i], ref offspring_pop[count_Child + i], num_Genens, 1);
}
count_Child = count_Child + lst_Child.Count;
}
//ii. Evaluate the individuals in offspring-pop for selected optimization tasks only (see Algorithm 3)
bst_Idx = -1;
for (int i = 0; i < pop_Size; i++)
{
evaluate.evaluate(tsk, 1, lambda, ref offspring_pop[i], ref count_Evaluate);
number_of_evaluations = number_of_evaluations + count_Evaluate;
if (offspring_pop[i].Factorial_Cost[0] < best_Fitness_In_Generation)
{
best_Fitness_In_Generation = offspring_pop[i].Factorial_Cost[0];//do chi co 1 task
bst_Idx = i;
}
termination_Condition.Number_of_evaluations = number_of_evaluations;
//Nếu đủ điều kiện thì dừng
if (!termination_Condition.checkTerminationCondition())
{
break;
}
}
if (bst_Idx != -1)
{
mfea_Class.copy_Individual(offspring_pop[bst_Idx], ref best_Ind_data, num_Genens, 1);
}
ev_Best_Fitness.Add(best_Fitness_In_Generation);
//iii. Concatenate offspring-pop and current-pop to form an intermediate-pop (P ∪ C).
for (int i = 0; i < pop_Size; i++)
{
mfea_Class.copy_Individual(current_pop[i], ref c_pop[i], num_Genens, 1);
mfea_Class.copy_Individual(offspring_pop[i], ref c_pop[i + pop_Size], num_Genens, 1);
}
//v. Select the fittest individuals from intermediate-pop to form the next current-pop (P).
if (string.Equals(selection_pressure, "elitist"))
{
Array.Sort(c_pop, new SortAscFactorialCostIdx());
for (int j = 0; j < pop_Size; j++)
{
mfea_Class.copy_Individual(c_pop[j], ref current_pop[j], num_Genens, 1);
}
}
else
{
if (string.Equals(selection_pressure, "roulette wheel"))
{
}
}
//Local search
if (!string.IsNullOrEmpty(local_Search_Method) && (local_Search_Method.Trim() != "NONE"))
{
for (int i = 0; i < pop_Size; i++)
{
local_Search.local_search_alg(tsk[0], local_Search_Method, num_Genens, rnd, ref offspring_pop[i], ref count_Evaluate);
number_of_evaluations = number_of_evaluations + count_Evaluate;
termination_Condition.Number_of_evaluations = number_of_evaluations;
//Nếu đủ điều kiện thì dừng
if (!termination_Condition.checkTerminationCondition())
{
break;
}
}
}
diversity_of_Pop = evaluate.compute_Population_Diversity(current_pop, pop_Size, num_Genens, population_Diversity_Method);
diversity_of_pop_in_Gers.Add(diversity_of_Pop);
generation++;
frmCmdOut.ClearFromPosToEndOfCurrentLine(50);
Console.Write(String.Format("{0, 7} | {1, 13} |", generation, ""));
termination_Condition.Number_of_evaluations = number_of_evaluations;
} //while
Data_MFEA data_MFEA = new Data_MFEA(num_Genens, 1);
mfea_Class.copy_Individual(best_Ind_data, ref data_MFEA.Best_Ind_Data[0], num_Genens, 1);
foreach (double items in ev_Best_Fitness)
{
double[] tmp = new double[1];
tmp[0] = items;
data_MFEA.Ev_Best_Fitness.Add(tmp);
}
foreach (double items in diversity_of_pop_in_Gers)
{
data_MFEA.Diversity_of_Pop.Add(items);
}
return(data_MFEA);
}
