public void MFO_Mutation(Mutation_Parameters mutation_Para, ref Chromosome ind, int num_Genes, Tasks tsk, Random rnd)
{
switch (mutation_Para.Mutation_Name)
{
case "SLIGHT_MUTATE":
ind.Rnvec = slight_Mutate(ind.Rnvec, num_Genes, mutation_Para.Sigma, rnd);
break;
case "SWAP_MUTATION":
ind.Invec = swap_Mutation(ind.Invec, num_Genes, mutation_Para.Mutation_Rate, rnd);
break;
case "PRUFER_SWAP_MUTATION":
ind.Invec = swap_Mutation(ind.Invec, num_Genes, mutation_Para.Mutation_Rate, rnd);
break;
case "EDGE_MUTATION":
ind.Edges_Matrix = edge_Mutation(ind.Edges_Matrix, num_Genes, mutation_Para.Mutation_Rate, rnd);
break;
case "EDGE_CLUSTERED_TREE_MUTATION":
#region EDGE_CLUSTERED_TREE_MUTATION
ind.Edges_Matrix = edge_Clustered_Tree_Mutation(ind.Edges_Matrix, num_Genes, mutation_Para.Mutation_Rate, tsk.Num_Cluster, tsk.Vertex_In_Cluster, rnd);
#endregion
break;
}
return;
}
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);
}
