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);
}
public void GA_Alg(string[] args)
{
#region Khai bao
string tmpStr = "",
all_Instances = "",
tempPath,
folderResults,
instance_name = "",
selection_pressure = "",
problem_Instance_Name, /* The name of instance of the optimization problem. */
saveFile,
folder_Results,
initial_Individual_Alg = "",
local_Search_Alg = "",
population_Diversity_Alg = "",
algorithm_Index = "",
parameter_File = "";
int population_size = 0, /* The size of the population. */
selection_size = 0, /* The size of the selection. */
random_seed = 0, /* The seed used for the random-number generator. */
number_of_Vertex = 0, /* The number of vertex in graph.*/
problem_index = 0, /* The index of the optimization problem. */
max_Vertex = 0, /* Số gene tối đa của các TASK <-> Số gen cua nhiễm sắc thể*/
type_encoding_in_union_search_space = 0, /* Kiểu mã hóa được sử dụng trong không gian chung giữa các task (sử dụng khi khở tạo quần thể) => có thể khác problem_index của các task*/
number_Runs = 0; /* Số lần chạy <-> số seed */
double fitness_variance_tolerance, /* The minimum fitness variance level that is allowed. */
vtr, /* The value-to-reach (function value of best solution that is feasible). */
best_ever_evaluated_objective_value, /* The best ever evaluated objective value. */
best_ever_evaluated_constraint_value, /* The best ever evaluated constraint value. */
best_prevgen_objective_value, /* Objective value of best solution in all previous generations. */
best_prevgen_constraint_value, /* Constraint value of best solution in all previous generations. */
bstFitness,
lambda = 0.0f,
crossover_Rate,
mutation_Rate;
byte use_vtr; /* Whether to terminate at the value-to-reach (VTR) (0 = no). */
int[] bstTour;
double[] bestFitness;
Tasks[] tasks = new Tasks[1]; /* Information about data of each tasks */
tasks[0] = new Tasks();
string pattern = " ";
Regex myRegex = new Regex(pattern);
Regex rg = new Regex(@"\s+");
#endregion
//Prufer_Instance_Data[] instances_Weights; /* Edges weight matrix of an instance*/
#region Read parameters
try
{
if (args.Length <= 0)
{
Console.Write("Number of parameters is incorrect, require > 9 parameters (you provided %d).\n\n", args.Length);
print_Usage();
}
for (int i = 0; i < args.Length; i++)
{
args[i] = args[i].Trim();
args[i] = rg.Replace(args[i], @" ");
args[i] = args[i].Replace(" ", "");
if (string.IsNullOrEmpty(args[i]))
{
continue;
}
#region PARAMETER_FILE
if (args[i].IndexOf("PARAMETER_FILE") != -1)
{
parameter_File = mfea_Class.getSubStringToEnd(args[i], "=");
continue;
}
#endregion
#region ALGORITHM_INDEX
if (args[i].IndexOf("ALGORITHM_INDEX") != -1)
{
algorithm_Index = mfea_Class.getSubStringToEnd(args[i], "=");
continue;
}
#endregion
#region RUNS
if (args[i].IndexOf("RUNS") != -1)
{
number_Runs = Convert.ToInt32(mfea_Class.getSubStringToEnd(args[i], "="));
if (number_Runs < 1)
{
Console.WriteLine(" Entered seed value is wrong, value of number of runs must > 0 \n");
return;
}
continue;
}
#endregion
#region SEED
if (args[i].IndexOf("SEED") != -1)
{
random_seed = Convert.ToInt32(mfea_Class.getSubStringToEnd(args[i], "="));
if (random_seed <= 0.0)
{
Console.WriteLine(" Entered seed value is wrong, seed value must > 0 \n");
return;
}
//rnd = new Random(random_seed);
continue;
}
#endregion
#region POPULATION_SIZE
if (args[i].IndexOf("POPULATION_SIZE") != -1)
{
population_size = Convert.ToInt32(mfea_Class.getSubStringToEnd(args[i], "="));
if (population_size < 4 || (population_size % 2) != 0)
{
if (population_size < 4)
{
Console.WriteLine(" population size read is : %d", population_size);
Console.WriteLine("\n Wrong population size entered, hence exist \n");
return;
}
if (population_size % 2 == 1)
{
Console.WriteLine(" population size read is : %d", population_size);
Console.WriteLine("\n Wrong population size entered, hence fix \n");
population_size = population_size + 1;
}
}
continue;
}
#endregion
#region NUMBER_OF_EVALUATE
if (args[i].IndexOf("NUMBER_OF_EVALUATE") != -1)
{
maximum_number_of_evaluations = Convert.ToInt64(mfea_Class.getSubStringToEnd(args[i], "="));
if (number_of_evaluations < 1)
{
Console.WriteLine("\n number of generations read is : %d", maximum_number_of_evaluations);
Console.WriteLine("\n Wrong number of generations entered, hence exiting \n");
return;
}
continue;
}
#endregion
#region CROSSOVER_RATE
if (args[i].IndexOf("CROSSOVER_RATE") != -1)
{
crossover_Rate = Convert.ToDouble(mfea_Class.getSubStringToEnd(args[i], "="));
if (crossover_Rate <= 0.0 || crossover_Rate >= 1.0)
{
Console.WriteLine(" Entered rmp value is wrong, rmp value must be in (0,1) \n");
return;
}
crss_Para.Rmp = crossover_Rate;
continue;
}
#endregion
#region MUTATION_RATE
if (args[i].IndexOf("MUTATION_RATE") != -1)
{
mutation_Rate = Convert.ToDouble(mfea_Class.getSubStringToEnd(args[i], "="));
if (mutation_Rate <= 0.0 || mutation_Rate >= 1.0)
{
Console.WriteLine(" Entered mutation rate value is wrong, mutation rate value must be in (0,1) \n");
return;
}
mtt_Para.Mutation_Rate = mutation_Rate;
continue;
}
#endregion
#region LAMBDA
if (args[i].IndexOf("LAMBDA") != -1)
{
lambda = Convert.ToDouble(mfea_Class.getSubStringToEnd(args[i], "="));
if (lambda < 0.0)
{
Console.WriteLine("\n Wrong number of Lambda, hence exiting \n");
return;
}
continue;
}
#endregion
#region SELECTION_PRESSURE
if (args[i].IndexOf("SELECTION_PRESSURE") != -1)
{
selection_pressure = mfea_Class.getSubStringToEnd(args[i], "=");
if (string.IsNullOrEmpty(selection_pressure))
{
Console.WriteLine("\n Wrong selection pressure \n");
return;
}
continue;
}
#endregion
#region INITIAL_INDIVIDUAL_ALGORITHM
if (args[i].IndexOf("INITIAL_INDIVIDUAL_ALGORITHM") != -1)
{
initial_Individual_Alg = mfea_Class.getSubStringToEnd(args[i], "=");
continue;
}
#endregion
#region CROSSOVER_NAME
if (args[i].IndexOf("CROSSOVER_NAME") != -1)
{
crss_Para.Crossver_Name = mfea_Class.getSubStringToEnd(args[i], "=");
continue;
}
#endregion
#region MUTATION_NAME
if (args[i].IndexOf("MUTATION_NAME") != -1)
{
mtt_Para.Mutation_Name = mfea_Class.getSubStringToEnd(args[i], "=");
continue;
}
#endregion
#region LOCAL_SEARCH_ALGORITHM
if (args[i].IndexOf("LOCAL_SEARCH_ALGORITHM") != -1)
{
local_Search_Alg = mfea_Class.getSubStringToEnd(args[i], "=");
continue;
}
#endregion
#region POPULATION_DIVERSITY_ALGORITHM
if (args[i].IndexOf("POPULATION_DIVERSITY_ALGORITHM") != -1)
{
population_Diversity_Alg = mfea_Class.getSubStringToEnd(args[i], "=");
continue;
}
#endregion
#region Read_Info_Of_Task
if (args[i].IndexOf("INSTANCE_NAME") != -1)
{
//int idx = Convert.ToInt32(args[i].Substring(args[i].LastIndexOf("_") + 1, 1))-1;
instance_name = mfea_Class.getSubStringToEnd(args[i], "=");
tasks[0].Data_file_path = instance_name;
//Tach lay ten file bo duong dan
instance_name = Path.GetFileNameWithoutExtension(Path.GetFileName(instance_name));
instance_name = Char.ToUpperInvariant(instance_name[0]) + instance_name.Substring(1);
all_Instances = instance_name;//Tên các bộ dữ liệu Test -> tạo thư mục lưu kết quả
continue;
}
//Lấy thông tin về kiểu file dữ liệu input
if (args[i].IndexOf("INSTANCE_TYPE") != -1)
{
tasks[0].Function_Cost_Name = mfea_Class.getSubStringToEnd(args[i], "=");
continue;
}
#endregion
}//for
}
catch
{
Console.Write("Error parsing parameters.\n\n");
print_Usage();
}
//Đọc dữ liệu từng bài toán
//Index đầu tiên là: problem_index (kiểu bài toán) ---> index tiếp theo là: tên file dữ liệu -> bai toan 1 - 8 thi bo qua ten file du lieu
if (!File.Exists(tasks[0].Data_file_path))
{
Console.WriteLine("Do not exist file: " + tasks[0].Data_file_path);
return;
}
ioFile.read_Data(ref tasks[0]);
tasks[0].MaxDims = tasks[0].Dims;
#endregion
tmpStr = all_Instances;
//Ghi thong tin ra file
folderResults = @"Results";
Directory.CreateDirectory(folderResults);
all_Instances = string.Format("Para_File({0})_Instance({1})", Path.GetFileNameWithoutExtension(parameter_File), all_Instances);
folderResults = @"Results\" + all_Instances;
Directory.CreateDirectory(folderResults);
//Neu da co file ket qua thi ko chay tiep
//tempPath = folderResults + @"\" + all_Instances + @".opt";
//if (File.Exists(tempPath))
//{
// Console.WriteLine();
// return;
//}
//Điều kiện dừng
termination_Condition.Maximum_number_of_evaluations = maximum_number_of_evaluations;
termination_Condition.Number_of_evaluations = 0;
if (number_Runs <= 1)
{
if (random_seed == 1)
{
Console.WriteLine("-----------------------------------------------------------------------------");
Console.WriteLine("| Algorithm | Instances | Seed | Geners | Best Cost|");
Console.WriteLine("-----------------------------------------------------------------------------");
}
Console.Write(String.Format("| {0,-9} | {1,-25} | {2,4} |", "GA_" + algorithm_Index.ToString(), "", random_seed));
//Console.SetCursorPosition(13, Console.CursorTop);
//Console.Write(String.Format(" {0, 0} ", tmpStr));
instance_name = Path.GetFileNameWithoutExtension(Path.GetFileName(tasks[0].Data_file_path));
tempPath = folderResults + @"\" + string.Format("Para_File({0})_", Path.GetFileNameWithoutExtension(parameter_File)) + "Instance_" + "(" + instance_name + @")_Seed(" + random_seed.ToString() + ").opt";
if (File.Exists(tempPath))
{
return;
}
stGA.Reset();
stGA.Start();
rnd = new Random(random_seed);
Data_MFEA final_Results = main_GA_1(initial_Individual_Alg, termination_Condition, population_size, tasks, lambda, crss_Para, mtt_Para,
selection_pressure, local_Search_Alg, population_Diversity_Alg, rnd);
Console.WriteLine();
stGA.Stop();
instance_name = Path.GetFileNameWithoutExtension(Path.GetFileName(tasks[0].Data_file_path));
instance_name = Char.ToUpperInvariant(instance_name[0]) + instance_name.Substring(1);
tempPath = folderResults + @"\" + string.Format("Para_File({0})_", Path.GetFileNameWithoutExtension(parameter_File)) + "Instance" + "(" + instance_name + @")_Seed(" + random_seed.ToString() + ").opt";
//ioFile.write_Solution_to_File(tempPath, random_seed, final_Results.Best_Ind_Data[i].Rnvec, tasks[i].Dims, final_Results.Best_Ind_Data[i].Obj_value[i]);
ioFile.write_Results(tempPath, random_seed, tasks[0], 0, final_Results.Best_Ind_Data[0], stGA.Elapsed.ToString());
//Console.WriteLine(string.Format("| {0,-9} | {1,-25} | {2,4} | {3,9}| {4,15} |", "", "+ " + instance_name, "", "", final_Results.Best_Ind_Data[0].Obj_value[0].ToString("#.##")));
Console.SetCursorPosition(60, Console.CursorTop);
Console.Write(string.Format("{0,15}", final_Results.Best_Ind_Data[0].Obj_value[0].ToString("0.00")));
Console.WriteLine();
tempPath = folderResults + @"\" + all_Instances + "_Seed(" + random_seed.ToString() + ").gen";
ioFile.write_Results_In_Generations(tempPath, final_Results.Ev_Best_Fitness, 1);
tempPath = folderResults + @"\" + all_Instances + "_Seed(" + random_seed.ToString() + ").div";
ioFile.write_Parameters_In_Generations(tempPath, final_Results.Diversity_of_Pop, "Pop_Diversity");
}
else
{
instance_name = Path.GetFileNameWithoutExtension(Path.GetFileName(tasks[0].Data_file_path));
//tempPath = folderResults + @"\" + string.Format("Para_File({0})_", Path.GetFileNameWithoutExtension(parameter_File)) + "Instance" + "(" + instance_name + @")_Seed(" + (1).ToString() + ").opt";
//if (File.Exists(tempPath))
//{
// return;
//}
double sum_Best_Fitness = 0.0f;
for (int j = 1; j <= number_Runs; j++)
{
if (j == 1)
{
Console.WriteLine("-----------------------------------------------------------------------------");
Console.WriteLine("| Algorithm | Instances | Seed | Geners | Best Cost|");
Console.WriteLine("-----------------------------------------------------------------------------");
}
tempPath = folderResults + @"\" + string.Format("Para_File({0})_", Path.GetFileNameWithoutExtension(parameter_File)) + "Instance" + "(" + instance_name + @")_Seed(" + j.ToString() + ").opt";
Console.Write(String.Format("| {0,-9} | {1,-25} | {2,4} |", "GA_" + algorithm_Index.ToString(), "", j));
Console.SetCursorPosition(13, Console.CursorTop);
Console.Write(String.Format(" {0, 0} ", tmpStr));
if (File.Exists(tempPath))
{
Console.WriteLine();
continue;
}
stGA.Reset();
stGA.Start();
rnd = new Random(j);
Data_MFEA final_Results = main_GA_1(initial_Individual_Alg, termination_Condition, population_size, tasks, lambda, crss_Para, mtt_Para,
selection_pressure, local_Search_Alg, population_Diversity_Alg, rnd);
stGA.Stop();
//Console.SetCursorPosition(60, Console.CursorTop);
//Console.Write(string.Format("{0,15}", "---------------"));
//Console.WriteLine();
instance_name = Path.GetFileNameWithoutExtension(Path.GetFileName(tasks[0].Data_file_path));
instance_name = Char.ToUpperInvariant(instance_name[0]) + instance_name.Substring(1);
tempPath = folderResults + @"\" + string.Format("Para_File({0})_", Path.GetFileNameWithoutExtension(parameter_File)) + "Instance" + "(" + instance_name + @")_Seed(" + j.ToString() + ").opt";
//ioFile.write_Solution_to_File(tempPath, random_seed, final_Results.Best_Ind_Data[i].Rnvec, tasks[i].Dims, final_Results.Best_Ind_Data[i].Obj_value[i]);
ioFile.write_Results(tempPath, j, tasks[0], 0, final_Results.Best_Ind_Data[0], stGA.Elapsed.ToString());
Console.SetCursorPosition(60, Console.CursorTop);
Console.Write(string.Format("{0,15}", final_Results.Best_Ind_Data[0].Obj_value[0].ToString("0.00")));
Console.WriteLine();
tempPath = folderResults + @"\" + all_Instances + "_Seed(" + j.ToString() + ").gen";
ioFile.write_Results_In_Generations(tempPath, final_Results.Ev_Best_Fitness, 1);
tempPath = folderResults + @"\" + all_Instances + "_Seed(" + j.ToString() + ").div";
ioFile.write_Parameters_In_Generations(tempPath, final_Results.Diversity_of_Pop, "Pop_Diversity");
sum_Best_Fitness = sum_Best_Fitness + final_Results.Best_Ind_Data[0].Obj_value[0];
tempPath = folderResults + @"\" + string.Format("Para_File({0})_", Path.GetFileNameWithoutExtension(parameter_File)) + "Instance" + "(" + instance_name + ").opt";
ioFile.writeOptimalSolutionofSeed(tempPath, j, final_Results.Best_Ind_Data[0].Obj_value[0], "");
}//for
sum_Best_Fitness = sum_Best_Fitness / number_Runs;
tempPath = folderResults + @"\" + string.Format("Para_File({0})_", Path.GetFileNameWithoutExtension(parameter_File)) + "Instance" + "(" + instance_name + ").opt";
ioFile.writeOptimalSolutionofSeed(tempPath, -1, sum_Best_Fitness, "");
}//if
}
