public static void Test_Genetic(string mesh_type)
{
int[] sizes_mesh = new int[21] {
10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50
};
int[] final_positions = new int[21] {
99, 143, 195, 255, 323, 399, 483, 575, 675, 783, 899, 1023, 1155, 1295, 1443, 1599, 1763, 1935, 2115, 2303, 2499
};
for (int i = 0; i < sizes_mesh.Length; i++)
{
string size = "_" + sizes_mesh[i] + "x" + sizes_mesh[i] + ".txt";
string file_name = mesh_type + size;
Console.WriteLine("Init Execution -> " + file_name);
//-------------------------------------------------------------------
//Create the mesh environment
MeshEnvironment env = new MeshEnvironment(_start: 0, _final: final_positions[i], _file_name: file_name, sizes_mesh[i]);
env.InitEnviroment(type_mesh: mesh_type);
//-------------------------------------------------------------------
for (int j = 0; j < num_test; j++)
{
env.FillDistanceMatrix();
DijkstraAlgorithm dk1 = new DijkstraAlgorithm();
dk1.Execute(ref env);
GeneticAlgorithm gn1 = new GeneticAlgorithm();
gn1.Execute(ref env);
//Insert the obstacle in a middle zone of the current optimal solution
InsertSeveralObstacle(ref env, num_obstacle: 2, num_routes: 2);
env.FillDistanceMatrix();
DijkstraAlgorithm dk2 = new DijkstraAlgorithm();
dk2.Execute(ref env);
GeneticAlgorithm gn2 = new GeneticAlgorithm();
gn2.Execute(ref env);
////Store variables in a txt
Console.WriteLine("Execution {0} of " + file_name, j);
StoreVariable_Genetic(mesh_type, ref env, ref dk1, ref gn1, ref dk2, ref gn2);
env.ClearObstacles();
}
Console.WriteLine("End Execution -> " + file_name);
Console.WriteLine("---------------------------------------------");
}
}
public static void InsertSeveralObstacle(ref MeshEnvironment env, int num_obstacle, int num_routes)
{
for (int i = 0; i < num_routes; i++)
{
env.FillDistanceMatrix();
DijkstraAlgorithm dk = new DijkstraAlgorithm();
dk.Execute(ref env);
//Ver bien como poner esto
env.InsertObstacle(ref dk.final_best_path, num_obstacle);
}
}
//---------------------------------------------------------------------
public static void StoreVariable_RW1_and_ACOv0(string mesh_type, ref MeshEnvironment env, ref AntColonyOptimizationv0 aco, ref RandomWalkv1 rw1)
{
env.FillDistanceMatrix();
DijkstraJustCost dijkstra = new DijkstraJustCost();
Double best_cost = dijkstra.DijkstraAlgo(graph: env.distances, source: env.start_node, verticesCount: env.final_node + 1);
Double percentage_learning = MeasureFunctions.CalculatePercentageLearning(ref env);
string separator = " ";
string line = mesh_type + separator;
line += env.world.Count + separator;
line += env.edges.Count + separator;
line += best_cost + separator;
line += aco.max_ants + separator;
line += aco.alpha + separator;
line += aco.beta + separator;
line += aco.tau_0 + separator;
line += aco.evaporation_factor + separator;
line += aco.percentage_convergence + separator;
line += aco.episode_counter + separator;
line += aco.execution_time + separator;
line += aco.stuck_roads + separator;
line += MeasureFunctions.GetVisitedNodes(ref env) + separator;
line += MeasureFunctions.GetVisitedEdges(ref env) + separator;
line += Math.Round(aco.best_cost, 2) + separator;
line += percentage_learning + separator;
line += rw1.num_random_walks + separator;
line += rw1.random_movements + separator;
line += rw1.evaporation_factor + separator;
line += rw1.best_cost + separator;
line += rw1.execution_time;
string path = @"data_sets/results/acov0_rw1/" + mesh_type + "/";
string variables_file = "variablesarw1" + "_" + mesh_type + "_" + env.size + "x" + env.size + ".txt";
using (StreamWriter file =
File.AppendText(path + variables_file))
{
file.WriteLine(line);
}
string path2 = @"data_sets/results/acov0_rw1/";
string variables_file2 = "variablesrw1" + "_" + mesh_type + ".txt";
using (StreamWriter file =
File.AppendText(path2 + variables_file2))
{
file.WriteLine(line);
}
}
public static void Test_RWv2_ACOv0(string mesh_type)
{
int[] sizes_mesh = new int[21] {
10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50
};
int[] final_positions = new int[21] {
99, 143, 195, 255, 323, 399, 483, 575, 675, 783, 899, 1023, 1155, 1295, 1443, 1599, 1763, 1935, 2115, 2303, 2499
};
for (int i = 0; i < sizes_mesh.Length; i++)
{
string size = "_" + sizes_mesh[i] + "x" + sizes_mesh[i] + ".txt";
string file_name = mesh_type + size;
Console.WriteLine("Init Execution -> " + file_name);
//-------------------------------------------------------------------
//Create the mesh environment
MeshEnvironment env = new MeshEnvironment(_start: 0, _final: final_positions[i], _file_name: file_name, sizes_mesh[i]);
env.InitEnviroment(type_mesh: mesh_type);
//-------------------------------------------------------------------
for (int j = 0; j < num_test; j++)
{
//............................................................................
// ROUTE 1
env.FillDistanceMatrix();
DijkstraAlgorithm dk1 = new DijkstraAlgorithm();
dk1.Execute(ref env);
RandomWalkv2 rw1 = new RandomWalkv2();
rw1.ExecuteRandomWalk(ref env);
AntColonyOptimizationv0 aco1 = new AntColonyOptimizationv0(_random_walk: true);
aco1.ExecuteACO(ref env);
//............................................................................
//Insert the obstacle in a middle zone of the current optimal solution
InsertSeveralObstacle(ref env, num_obstacle: 2, num_routes: 2);
//............................................................................
// ROUTE 2 - REROUTING
env.FillDistanceMatrix();
DijkstraAlgorithm dk2 = new DijkstraAlgorithm();
dk2.Execute(ref env);
RandomWalkv2 rw2 = new RandomWalkv2();
rw2.ExecuteRandomWalk(ref env);
AntColonyOptimizationv0 aco2 = new AntColonyOptimizationv0(_random_walk: true);
aco2.ExecuteACO(ref env);
//............................................................................
////Store variables in a txt
Console.WriteLine("Execution {0} of " + file_name, j);
StoreVariable_RWv2_ACOv0(mesh_type, ref env, ref dk1, ref aco1, ref dk2, ref aco2);
//............................................................................
//Reset environment
env.InitPheromones(0);
env.ClearObstacles();
}
Console.WriteLine("End Execution -> " + file_name);
Console.WriteLine("---------------------------------------------");
}
}
//-------------------------------------------------------------------------------
public void Execute(ref MeshEnvironment env)
{
//Fill distances to objective
env.FillDistancesToFinal();
//Init the node tags list;
int size = env.size * env.size;
InitNodeTags(size);
//Get the distance between node. Adjacency Matrix
env.FillDistanceMatrix();
Double[,] distances_matrix = env.distances;
//Get initial and final nodes
int initial_position = env.start_node;
int final_position = env.final_node;
//Get world of the env
List <Node> world = env.world;
//Init the values of the initial node and its tag
node_tags[initial_position].local_goal = 0.0;
node_tags[initial_position].global_goal = MeasureHeuristic(initial_position, ref env);
//Fijar la posicion actual
Tag current_position = node_tags[initial_position];
//Nodos que seran testeados
List <Tag> tested_nodes_list = new List <Tag>();
tested_nodes_list.Add(node_tags[initial_position]);
//Time variable
var watch = System.Diagnostics.Stopwatch.StartNew();
while (tested_nodes_list.Count != 0) // && current_position.node_id != env.final_node)
{
//Ordenar la lista de acuerdo al objetivo global en orden ascendente
tested_nodes_list = tested_nodes_list.OrderBy(tag => tag.global_goal).ToList();
//Remover de la lista si el nodo analizado ya ha sido visitado
while (tested_nodes_list.Count != 0 && tested_nodes_list[0].visited)
{
tested_nodes_list.RemoveAt(0);
}
//Si la lista es vacia salimos del bucle
if (tested_nodes_list.Count == 0)
{
break;
}
//Inicializamos nuestra posicion actual
current_position = tested_nodes_list[0];
current_position.visited = true;
tested_nodes_list[0] = current_position;
node_tags[current_position.node_id] = current_position;
//Obtengo los posibles nodos a los cuales me puedo mover
List <int> list_posible_nodes = GetPosibleNodes(current_position.node_id, ref env);
//Itero por los posibles nodos
foreach (var neighbour_id in list_posible_nodes)
{
int current_id = current_position.node_id;
Double possible_lower_goal = current_position.local_goal + distances_matrix[current_id, neighbour_id];
if (possible_lower_goal < node_tags[neighbour_id].local_goal && !node_tags[neighbour_id].visited)
{
Tag new_tag = node_tags[neighbour_id];
new_tag.parent = current_id;
new_tag.local_goal = possible_lower_goal;
new_tag.global_goal = new_tag.local_goal + MeasureHeuristic(neighbour_id, ref env);
node_tags[neighbour_id] = new_tag;
if (tested_nodes_list.Contains(new_tag))
{
int idx = tested_nodes_list.IndexOf(new_tag);
tested_nodes_list[idx] = new_tag;
}
}
//Si los nodos analizados aun no han sido marcados como visitados
//Los añadimos a lista de testeo
if (!node_tags[neighbour_id].visited && !tested_nodes_list.Contains(node_tags[neighbour_id]))
{
tested_nodes_list.Add(node_tags[neighbour_id]);
}
}
}
List <int> optimal_path = GetPath(final_position, initial_position);
Double optimal_cost = ExtraTools.GetCost(ref optimal_path, ref env);
final_best_path = optimal_path;
final_best_path.Reverse();
final_best_cost = optimal_cost;
//Execution Time
watch.Stop();
execution_time = watch.ElapsedMilliseconds;
}
