private void MFO_All_Method(string[] args)
{
if ((args == null) || (args.Length < 1))
{
Console.WriteLine("Khong co tham so");
}
else
{
Random ran;
int numCluster;
int numCity;
int depot;
string nameInstance;
string folderResults;
string saveFile = "";
string tempPath = "";
int[] lhkTour;
double bestCost = 0.0f;
int[] tour;
double[] mi;
double m;
double[,] tmpWeight;
string[] args_In;
string str_Path;
Stopwatch stGA = new Stopwatch();
//string alg_Index = getSubStringToEnd(args[1].Trim(), "=");
switch (Convert.ToInt32(args[0]))
{
case 1:
#region MFO
/*********************************************************************************************************************************************
* MFO: giai_thuat(0); File_Parameters(1);
* num_KP_TASK(k);...
*
********************************************************************************************************************************************/
//if (int.Parse(args[1]) == 1)
//{
// Console.WriteLine("-----------------------------------------------------------------------------");
// Console.WriteLine("| Algorithm | Instances | Seed | Geners | Best Cost|");
// Console.WriteLine("-----------------------------------------------------------------------------");
//}
//Console.Write(String.Format("| {0,-9} | {1,-25} | {2,4} |", "Real_MFO", "", args[1]));
args_In = ioFile.read_Parameters_File(args[1]);
mfea_instance.MFEA_Alg(args_In);
break;
#endregion
case 2:
#region GA
/*********************************************************************************************************************************************
* MFO: giai_thuat(0); seed(1); popSize(2); numGeneration(3); rmp(4); mutation_rate(5); lambda(6); selection_pressure(7); num_TASKS(8);...<problem index>-<nameInstance>;
* MFO: giai_thuat(0); seed(1); popSize(2); numGeneration(3); rmp(4); lambda(5); num_TSP_TASK(6);...<nameInstance>;
* num_KP_TASK(k);...
*
********************************************************************************************************************************************/
args_In = ioFile.read_Parameters_File(args[1]);
ga_instance.GA_Alg(args_In);
break;
#endregion
case 3:
case 4:
#region Clustered_Tree
/*********************************************************************************************************************************************
* Giải thuật gần đúng cho bài toán Cluster Tree trong bài báo: M. D’Emidio, L. Forlizzi, D. Frigioni, S. Leucci, and G. Proietti,
* “On the Clustered Shortest-Path Tree Problem,” ICTCS 2016, p. 263, 2016.
*
* Parametes: Giai_thuat(0); Input_File_Name(1);
* Alg = 3: BFS+Dijstra
* Alg = 4: Prim + Prim
********************************************************************************************************************************************/
clustered_Tree.Heuristic_Clustered_Tree(args);
break;
#endregion
case 100:
#region Test
/*********************************************************************************************************************************************
*
* Test
*
********************************************************************************************************************************************/
int[] blob_Arr = new int[18] {
16, 4, 6, 2, 12, 0, 7, 0, 19, 3, 5, 17, 3, 16, 6, 12, 11, 9
};
int[] blob_Arr_1 = new int[21] {
-1, -1, 17, 5, 7, 3, 13, 1, 8, 1, 20, 4, 6, 18, 4, 17, 7, 13, 12, 10, -1
};
int[,] tree = new int[21, 21];
int[,] tree_1 = new int[20, 20];
break;
#endregion
case 101:
#region TSP Test
/*********************************************************************************************************************************************
* MFO: giai_thuat(0); seed(1); popSize(2); numGeneration(3); rmp(4); mutation_rate(5); lambda(6); selection_pressure(7);
* type_encoding_in_union_search_space(8); num_TASKS(8);...<problem index>-<nameInstance>;
*
********************************************************************************************************************************************/
if (int.Parse(args[1]) == 1)
{
Console.WriteLine("-----------------------------------------------------------------------------");
Console.WriteLine("| Algorithm | Instances | Seed | Geners | Best Cost|");
Console.WriteLine("-----------------------------------------------------------------------------");
}
Console.Write(String.Format("| {0,-9} | {1,-25} | {2,4} |", "Real_MFO", "", args[1]));
mfea_instance.MFEA_Alg(args);
#endregion
break;
case 102:
#region TSP Test
/*********************************************************************************************************************************************
* Tạo ngẫu nhiên optimal solution cho bài toán ONE MAX TREE
* MFO: giai_thuat(0); File_Name(1); Number_of_Vertex(2);
*
********************************************************************************************************************************************/
Initialize_Chromosome init_Chrome = new Initialize_Chromosome();
init_Chrome.create_Optimal_of_One_Max_Tree(args[1], Convert.ToInt32(args[2]));
#endregion
break;
case 103:
#region Chuyen do thi bieu dien duoi dang ma tran sang lenh Maplab
/*********************************************************************************************************************************************
* Đọc dữ liệu từ file input của bài toán One-Max-Tree: *.in -> vẽ đồ thị của cây của ma trận trong số
* giai_thuat(0); File_Name(1); Number_of_Vertex(2); =>
*
********************************************************************************************************************************************/
tmpWeight = ioFile.read_TSP_Instance(args[1], out numCity);
int[,] opt_tree = new int[numCity, numCity];
for (int i = 0; i < numCity; i++)
{
for (int j = 0; j < numCity; j++)
{
opt_tree[i, j] = (int)tmpWeight[i, j];
}
}
ioFile.draw_Plot_in_Matlab(Path.GetFileNameWithoutExtension(args[1]) + @".m", numCity, opt_tree);
#endregion
break;
case 104:
#region Tao instance cho bai toan CLUSTERED TREEE
/*********************************************************************************************************************************************
* Tao instance cho Clustered tree tu file TSP instance
* Parameters: giai_thuat(0); ten_file_TSP(1); ten_file_HTSP(2); seed(3); so_group(4); cac_gia_tri_group(...)
*
********************************************************************************************************************************************/
ran = new Random(Convert.ToInt32(args[3]));
int[] numCitiesInCluster = new int[Convert.ToInt32(args[4])];
for (int i = 0; i < Convert.ToInt32(args[4]); i++)
{
numCitiesInCluster[i] = Convert.ToInt32(args[4 + i + 1]);
}
ioFile.create_Clustered_Intance(args[1], args[2], numCitiesInCluster, ran);
#endregion
break;
case 105:
#region Tao instance cho bai toan CLUSTERED TREE từ bài toán Clustered TSP
/*********************************************************************************************************************************************
* Tao instance cho Clustered tree tu file Cluster TSP instance bằng cách thêm đỉnh nguồn
* Parameters: giai_thuat(0); ten_file_CTSP(1); Path_for_Save_Clustered_Tree_File(2);
*
********************************************************************************************************************************************/
str_Path = Path.GetFileName(args[1]);
if (string.IsNullOrWhiteSpace(args[2].Trim()))
{
str_Path = Path.GetFileNameWithoutExtension(str_Path) + @".clt";
}
else
{
if (!Directory.Exists(args[2]))
{
Directory.CreateDirectory(args[2]);
}
str_Path = args[2].Trim() + @"\" + Path.GetFileNameWithoutExtension(str_Path) + @".clt";
}
ioFile.create_Clustered_Tree_Instances_From_Clustered_TSP(args[1], str_Path);
#endregion
break;
case 106:
#region Tao file *.par tu file source_file cho GA giai bai toan Clustered Tree
/*********************************************************************************************************************************************
* Tao file *.par tu file source_file;
* Ten cua Final_File la Ten cua begin_Of_Name_Of_Final_File va file du lieu trong file_data
*
* Parmaters: alg(0); source_File(1); file_data(2); begin_Of_Name_Of_Final_File(3)
********************************************************************************************************************************************/
ioFile.creatate_Parameter_File_For_Cluster_Tree_GA(args[1], args[2], args[3]);
break;
#endregion
case 107:
#region Tao file *.par tu file source_file cho MFO giai bai toan Clustered Tree
/*********************************************************************************************************************************************
* Tao file *.par tu file source_file;
* Ten cua Final_File la Ten cua begin_Of_Name_Of_Final_File va file du lieu trong file_data
*
* Parmaters: alg(0); source_File(1); file_data(2); begin_Of_Name_Of_Final_File(3)
********************************************************************************************************************************************/
ioFile.creatate_Parameter_File_For_Cluster_Tree_MFO(args[1], args[2], args[3]);
break;
#endregion
case 108:
#region Tạo ma trận trọng số cho cluster có kích thước lớn nhất từ dữ liệu bài toán Clustered Tree
/*********************************************************************************************************************************************
* Parameters: alg(0); source_File(1); out_File(2);
*
********************************************************************************************************************************************/
double[,] weight_Matrix;
int[][] vertexInCluster;
int sourceVertex;
ioFile.read_File_Clusted_Tree(args[1], out weight_Matrix, out vertexInCluster, out numCity, out numCluster, out sourceVertex);
int idx_Max_Clus = 0;
int num_Vertex_In_Cluster = vertexInCluster[0].Length;
for (int i = 1; i < numCluster; i++)
{
if (vertexInCluster[i].Length > num_Vertex_In_Cluster)
{
num_Vertex_In_Cluster = vertexInCluster[i].Length;
idx_Max_Clus = i;
}
}
double[,] weight_Cluster_Matrix = init_Chrom.create_Weight_Matrix_for_Cluster(numCity, num_Vertex_In_Cluster, weight_Matrix, vertexInCluster[idx_Max_Clus]);
ioFile.create_Tree_Instance(args[2], num_Vertex_In_Cluster, weight_Cluster_Matrix);
break;
#endregion
case 109:
#region Tạo ma trận trọng số tu bai toan cluster de chay bai toan One-max tree cung
/*********************************************************************************************************************************************
* Parameters: alg(0); source_File(1); out_File(2);
* Example: 107 4Eil10.clt 4Eil10.in
********************************************************************************************************************************************/
double[,] weight_Matrix_1;
int[][] vertexInCluster_1;
int sourceVertex_1;
ioFile.read_File_Clusted_Tree(args[1], out weight_Matrix_1, out vertexInCluster_1, out numCity, out numCluster, out sourceVertex_1);
ioFile.create_Tree_Instance(args[2], numCity, weight_Matrix_1);
break;
#endregion
case 110:
#region Tong hop ket qua tu file *.opt; *.tour tu cac file trong thu muc (sử dụng cho thuật toán PRIM_PRIM (AAL))
/*********************************************************************************************************************************************
* Parameters: alg(0); Thu_muc_chua_ket_qua(1); Phan_mo_rong_cua_cac_file_ket_qua(2); ten_file_tong_hop(3)
* Example: 107 4Eil10.clt 4Eil10.in
********************************************************************************************************************************************/
Console.WriteLine("Dang doc du lieu tu thu muc: ");
Console.WriteLine(args[1]);
ioFile.statistic_Results(args[1], args[2], args[3]);
Console.WriteLine();
#endregion
break;
case 111:
#region Thống kê các instance của bài clustered tree, cluster lớn nhất có bao nhiêu đỉnh, chiếm bao nhiêu % số đỉnh
/*********************************************************************************************************************************************
* Parameters: alg(0); File_Danh_Sach_Instances(1); File_Thong_Ke(2); ten_file_tong_hop(3)
* Example: 107 4Eil10.clt 4Eil10.in
********************************************************************************************************************************************/
ioFile.count_Number_Of_Largest_Cluster_in_Instance(args[1], args[2]);
#endregion
break;
case 112:
#region Tao instance NON_EUCLIDEAN cho bai toan CLUSTERED TREE từ bài toán Clustered TSP
/*********************************************************************************************************************************************
* Tao instance NON_EUCLIDEAN cho Clustered tree tu file Cluster TSP instance bằng cách thêm đỉnh nguồn & tạo ma trân trọng số mới
* Parameters: giai_thuat(0); ten_file_CTSP(1); lowerNum(2); uppperNum(3); Path(Directory)_for_Save_Clustered_Tree_File(4);
*
********************************************************************************************************************************************/
str_Path = Path.GetFileName(args[1]);
if (string.IsNullOrWhiteSpace(args[2].Trim()))
{
str_Path = Path.GetFileNameWithoutExtension(str_Path) + @".clt";
}
else
{
if (!Directory.Exists(args[4]))
{
Directory.CreateDirectory(args[4]);
}
str_Path = args[4].Trim() + @"\" + Path.GetFileNameWithoutExtension(str_Path) + @".clt";
}
ioFile.create_NON_Euclidean_Clustered_Tree_Instances_From_Clustered_TSP(args[1], Convert.ToInt32(args[2]), Convert.ToInt32(args[3]), str_Path);
#endregion
break;
case 113:
#region Tính lại cost optimal solution của bài toán cluster shortest-path tree theo hàm mục tiêu đã sửa
/*********************************************************************************************************************************************
* Tình lại cost của lời giải bài toán cluster shortest-path tree theo hàm mục tiêu chuẩn
* Parameters: giai_thuat(0); file_Optmal_Solution(1); ;file_Out(3);
*
********************************************************************************************************************************************/
recompute_Cost_Opt_Solution_In_Folder(args[1], args[2], args[3]);
#endregion
break;
case 186:
#region aaa
//arg[1]: *.clt
//arg[2]: ma tran canh cua optimal solution
int dimension;
ioFile.read_File_Clusted_Tree(args[1], out weight_Matrix, out vertexInCluster, out dimension, out numCluster, out sourceVertex);
int[,] edge_matrix;
List <int> path_1;
double path_Length = 0;
read_Matrix_2D(args[2], dimension, out edge_matrix);
double[,] temp_Matrix = new double[dimension, dimension];
for (int i = 0; i < dimension; i++)
{
for (int j = 0; j < dimension; j++)
{
if ((edge_matrix[i, j] > 0) && (edge_matrix[i, j] < double.MaxValue)) //neu co canh
{
temp_Matrix[i, j] = weight_Matrix[i, j];
}
else
{
temp_Matrix[i, j] = 0;
}
}
}
Graph_Method graph_Method_Class = new Graph_Method();
int[] pre = graph_Method_Class.dijkstra(temp_Matrix, dimension, sourceVertex);
for (int i = 0; i < dimension; i++)
{
path_1 = graph_Method_Class.print_Path(sourceVertex, i, pre);
for (int l = path_1.Count - 1; l > 0; l--)
{
path_Length = path_Length + weight_Matrix[path_1[l], path_1[l - 1]];
}
}
double aaa = evaluation_Tree(weight_Matrix, edge_matrix, dimension, sourceVertex);
Console.WriteLine("Dijstra: " + path_Length);
Console.WriteLine("Mo: " + aaa);
Console.ReadKey();
//return path_Length;
#endregion
break;
}
Console.WriteLine();
}
}
