tractor moves using genetic_algorithm

agent/methods/genetic_algorithm.py

@@ -0,0 +1,113 @@
+import numpy as np
+import random
+import math
+
+def create_initial_population(num_cities, population_size, list):
+    population = []
+    for _ in range(population_size):
+        chromosome = list.copy()
+        chromosome.remove((1, 1))  # Usuń punkt (1, 1) z listy
+        random.shuffle(chromosome)
+        chromosome.insert(0, (1, 1))  # Dodaj punkt (1, 1) na początku trasy
+        population.append(chromosome)
+    return population
+
+def calculate_distance(city1, city2):
+    x1, y1 = city1
+    x2, y2 = city2
+    distance = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+    return distance
+
+def calculate_fitness(individual):
+    total_distance = 0
+
+    num_cities = len(individual)
+
+    for i in range(num_cities - 1):
+        city1 = individual[i]
+        city2 = individual[i + 1]
+        distance = calculate_distance(city1, city2)
+        total_distance += distance
+
+    fitness = 1 / total_distance
+
+    return fitness
+
+def crossover(parent1, parent2):
+    child = [(1, 1)] + [None] * (len(parent1) - 1)  # Inicjalizacja dziecka z punktem (1, 1) na początku
+    start_index = random.randint(1, len(parent1) - 1)
+    end_index = random.randint(start_index + 1, len(parent1))
+    # Skopiuj fragment miast od parent1 do dziecka
+    child[start_index:end_index] = parent1[start_index:end_index]
+    # Uzupełnij brakujące miasta z parent2
+    remaining_cities = [city for city in parent2 if city not in child]
+    child[1:start_index] = remaining_cities[:start_index - 1]
+    child[end_index:] = remaining_cities[start_index - 1:]
+    return child
+
+def mutate(individual, mutation_rate):
+    for i in range(1, len(individual)):  # Rozpoczynamy od indeksu 1, aby pominąć punkt (1, 1)
+        if random.random() < mutation_rate:
+            j = random.randint(1, len(individual) - 1)  # Wybieramy indeks od 1 do ostatniego indeksu
+            individual[i], individual[j] = individual[j], individual[i]
+    return individual
+
+def genetic_algorithm(list):
+    chromosome_length = 21
+    max_generations = 200
+    population_size = 200
+    crossover_rate = 0.25
+    mutation_rate = 0.1
+    num_cities = chromosome_length
+    population = create_initial_population(num_cities, population_size, list)
+
+    best_individual = None
+    best_fitness = float('-inf')
+
+    for generation in range(max_generations):
+        # # Oblicz wartości fitness dla każdego osobnika w populacji
+        # fitness_values = [calculate_fitness(individual) for individual in population]
+        # population = [x for _, x in sorted(zip(fitness_values, population), reverse=True)]
+        # fitness_values.sort(reverse=True)
+        # max_fitness_index = np.argmax(fitness_values)
+        # # Wybierz najlepszego osobnika z ostatniej populacji
+        # if fitness_values[max_fitness_index] > best_fitness:
+        #     best_fitness = fitness_values[max_fitness_index]
+        #     best_individual = population[max_fitness_index]
+       
+        # # Twórz nową populację z krzyżówek
+        # new_population = []
+        # for _ in range(int(population_size / 2)):
+        #     parent1, parent2 = random.choices(population[:population_size // 2], k=2)
+        #     child1 = crossover(parent1, parent2)
+        #     child2 = crossover(parent2, parent1)
+        #     new_population.extend([child1, child2])
+        # # Dokonaj mutacji na nowej populacji
+        # new_population = [mutate(individual, mutation_rate) for individual in new_population]
+        # population = new_population
+
+        # Oblicz wartości fitness dla każdego osobnika w populacji
+        fitness_values = [calculate_fitness(individual) for individual in population]
+        population = [x for _, x in sorted(zip(fitness_values, population), reverse=True)]
+        fitness_values.sort(reverse=True)
+        best_individuals = population[:10]  # Wybierz k najlepszych osobników
+        new_population = best_individuals.copy()
+
+        # Twórz nową populację z krzyżówek i mutacji
+        while len(new_population) < population_size:
+            parent1, parent2 = random.choices(best_individuals, k=2)  # Wybierz rodziców spośród najlepszych osobników
+            child = crossover(parent1, parent2)  # Krzyżowanie
+            child = mutate(child, mutation_rate)  # Mutacja
+            new_population.append(child)
+
+        for individual in best_individuals:
+            fitness = calculate_fitness(individual)
+            if fitness > best_fitness:
+                best_fitness = fitness
+                best_individual = individual
+
+        population = new_population[:population_size]
+
+        
+    print("Best path:", best_individual)
+    return best_individual

main.py

@@ -5,6 +5,8 @@ from pygame.locals import *
 from core.chicken import chicken as chick
 from core.field import field_settings
 from core.plants import plants_settings
+from agent.methods.genetic_algorithm import genetic_algorithm
+import numpy as np
 
 from agent.neural_network import inference
 #import agent.neural_network.inference
@@ -71,10 +73,19 @@ class Game:
         self.search_object = graph_search.Search(self.cell_size, self.cell_number)
         chicken_next_moves = []
 
-
         veggies = dict()
         veggies_debug = dict()
 
+        wheat_list = [obj for obj in self.plant_list if obj.name == "wheat" and obj.state == 0]
+
+        new_list = [()]
+        a = 1
+        for obj in wheat_list:
+            new_list.append ((obj.xy[0], obj.xy[1]))
+        
+        new_list[0] = (1, 1)
+        best_path = genetic_algorithm(new_list)
+
         while running:
             clock.tick(60)  # manual fps control not to overwork the computer
             for event in pygame.event.get():
@@ -95,14 +106,21 @@ class Game:
 
                 if event.type == move_chicken_event:
                     if len(chicken_next_moves) == 0:
-
                         angles = {0: 'UP', 90: 'RIGHT', 270: 'LEFT', 180: 'DOWN'}
                         closest_wheat = self.search_object.closest_point(self.chicken.x, self.chicken.y, 'wheat', self.plant_list)
-                        self.aim_list[0].xy[0] = closest_wheat[0]
-                        self.aim_list[0].xy[1] = closest_wheat[1]
-                        chicken_next_moves = self.search_object.astarsearch(
-                            [self.chicken.x, self.chicken.y, angles[self.chicken.angle]], [closest_wheat[0], closest_wheat[1]], self.stone_list, self.plant_list)
+                        # self.aim_list[0].xy[0] = closest_wheat[0]
+                        # self.aim_list[0].xy[1] = closest_wheat[1]
+                        self.aim_list[0].xy[0] = best_path[a][0]
+                        self.aim_list[0].xy[1] = best_path[a][1]
+                        # a += 1
+                        # target = wheat_list[a]
+                        # chicken_next_moves = self.search_object.astarsearch(
+                        #   [self.chicken.x, self.chicken.y, angles[self.chicken.angle]], [closest_wheat[0], closest_wheat[1]], self.stone_list, self.plant_list)
                         
+                        chicken_next_moves = self.search_object.astarsearch(
+                          [self.chicken.x, self.chicken.y, angles[self.chicken.angle]], [best_path[a][0], best_path[a][1]], self.stone_list, self.plant_list)
+
+                        a += 1
                         #neural_network
                         current_veggie = next(os.walk('./agent/neural_network/images/test'))[1][random.randint(0, len(next(os.walk('./agent/neural_network/images/test'))[1])-1)]
                         if(current_veggie in veggies_debug):