algorytm genetyczny połączony z resztą
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magdabiadala
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5dcaefe983
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3984da7585
@ -1,23 +1,17 @@
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import random
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import math
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# packs_coords
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# racks_coords
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### prawdopodobieństwo mutacji
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mutation_prob = 0.03
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### ilość osobników w pokoleniu, powinna być parzysta
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generation_size = 20
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generation_size = 40
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### liczba pokoleń
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number_of_generations = 30
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### liczba paczek
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number_of_packages = 45
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### liczba regałów
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number_of_racks = 70
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### jak bardzo promowane są osobniki wykorzystujące całą pojemność regału
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amount_of_promotion = 3
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amount_of_promotion = 0
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def first_gen():
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def first_gen(number_of_packages, number_of_racks):
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first_generation = []
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for individual in range(generation_size):
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individual = []
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@ -27,7 +21,7 @@ def first_gen():
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first_generation.append(individual)
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return first_generation
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def evaluation(individual):
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def evaluation(individual, packages, racks, number_of_packages, number_of_racks):
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# im większy fitness tym lepszy osobnik
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# print("regały: ",racks)
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rest_of_capacity = racks.copy()
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@ -46,11 +40,11 @@ def evaluation(individual):
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### tu dodaj to co zrobi Andrzej
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return fitness
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def roulette(generation):
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def roulette(generation, packages, racks, number_of_packages, number_of_racks):
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# print('pokolenie: ', generation)
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evaluations = []
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for i in range(generation_size):
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individual_fitness = evaluation(generation[i])
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individual_fitness = evaluation(generation[i], packages, racks, number_of_packages, number_of_racks)
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evaluations.append(individual_fitness)
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# print("tablica dopasowań: ", evaluations)
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maximum = min(evaluations)
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@ -76,7 +70,7 @@ def roulette(generation):
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# print('przetrwali: ',survivors)
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return survivors
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def crossover(individual1, individual2):
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def crossover(individual1, individual2, number_of_packages):
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cut = random.randint(1,number_of_packages-1)
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new1 = individual1[:cut]
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new2 = individual2[:cut]
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@ -89,21 +83,24 @@ def crossover(individual1, individual2):
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# print(cut)
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return new1, new2
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def mutation(individual):
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def mutation(individual, number_of_packages, number_of_racks):
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# print(individual)
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locus = random.randint(0,number_of_packages-1)
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individual[locus] = random.randint(0,number_of_racks-1)
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return individual
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def gen_alg(number_of_generations, generation_size, mutation_prob, amount_of_promotion):
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def gen_alg(packages, racks, number_of_generations, generation_size, mutation_prob, amount_of_promotion):
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number_of_packages = len(packages)
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number_of_racks = len(racks)
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### WŁAŚCIWY ALGORYTM
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generation = first_gen()
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generation = first_gen(number_of_packages, number_of_racks)
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global_maximum = -math.inf
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# pętla znajdująca najlepszy fitness w pierwszym pokoleniu
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for i in range(generation_size):
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evaluation_of_individual = evaluation(generation[i])
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evaluation_of_individual = evaluation(generation[i], packages, racks, number_of_packages, number_of_racks)
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if evaluation_of_individual > global_maximum:
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global_maximum = evaluation_of_individual
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best_individual = generation[i].copy()
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@ -114,13 +111,13 @@ def gen_alg(number_of_generations, generation_size, mutation_prob, amount_of_pro
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# print(generation)
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### RULETKA
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survivors = roulette(generation)
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survivors = roulette(generation, packages, racks, number_of_packages, number_of_racks)
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# print('przetrwali: ',survivors)
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### KRZYŻOWANIE
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descendants = []
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for individual in range(0,generation_size,2):
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pair = crossover(survivors[individual],survivors[individual+1])
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pair = crossover(survivors[individual],survivors[individual+1], number_of_packages)
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for each in pair:
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descendants.append(each)
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# print('potomkowie: ', descendants)
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@ -128,25 +125,21 @@ def gen_alg(number_of_generations, generation_size, mutation_prob, amount_of_pro
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### MUTACJA
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for individual in range(generation_size):
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if random.random() <= mutation_prob:
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mutation(descendants[individual])
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mutation(descendants[individual], number_of_packages, number_of_racks)
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# print('potomkowie po mutacji: ', descendants)
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### NAJLEPSZE DOPASOWANIE
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local_maximum = -math.inf
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for each in range(generation_size):
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specific_fitness = evaluation(descendants[each])
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specific_fitness = evaluation(descendants[each], packages, racks, number_of_packages, number_of_racks)
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if specific_fitness > local_maximum:
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local_maximum = specific_fitness
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print('maximum w pokoleniu: ',local_maximum)
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generation_best_individual = descendants[each].copy()
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print('maksimum w pokoleniu: ',local_maximum)
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if local_maximum > global_maximum:
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global_maximum = local_maximum
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best_individual = generation_best_individual.copy()
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generation = descendants
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print('maximum globalne: ', global_maximum)
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### lista paczek, indeks to id paczki, wartość w liście to jej waga
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packages = [random.randint(1,10) for i in range(number_of_packages)]
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### lista regałów, indeks to id regału, wartość w liście to jego pojemność
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racks = [random.randint(15,18) for i in range(number_of_racks)]
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# print(packages)
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# print(racks)
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gen_alg(number_of_generations, generation_size, mutation_prob, amount_of_promotion)
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print('maksimum globalne: ', global_maximum)
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print("jeśli maksimum globalne wynosi 0, każda paczka ma swój regał")
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print("najlepsze dopasowanie: ", best_individual)
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13
main.py
13
main.py
@ -6,11 +6,6 @@ import random
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import sys
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from attributes import PackStatus, COLORS, DIRECTION_ANGLES
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mutation_prob = 0.03
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generation_size = 20
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number_of_generations = 30
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amount_of_promotion = 3
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WINDOW_SIZE = (640, 640)
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COLOR_OF_FIELD = {
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'Floor': 'gray',
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@ -25,7 +20,7 @@ TILE_HEIGHT = 32
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CIRCLE_CENTER_X, CIRCLE_CENTER_Y = int(TILE_WIDTH/2), int(TILE_HEIGHT/2)
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class MainGameFrame:
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def __init__(self):
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def __init__(self, mutation_prob = 0.03, generation_size = 40, number_of_generations = 30, amount_of_promotion = 0):
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pygame.font.init()
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self.display = pygame.display.set_mode(WINDOW_SIZE)
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pygame.display.set_caption("Smart ForkLift")
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@ -40,12 +35,12 @@ class MainGameFrame:
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list_of_racks = self.warehouse_map.get_all_racks(True)
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racks_coords = [(line.x_position, line.y_position) for line in list_of_racks]
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packs_sizes = [pack.size for pack in self.warehouse_map.packages]
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# racks_capacities = [pack.size for pack in self.warehouse_map.packages]
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racks_capacities = [rack.capacity for rack in list_of_racks]
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print("koordynaty paczek: ",packs_coords)
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print("koordynaty regałów: ",racks_coords)
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print("wagi paczek: ",packs_sizes)
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# print("pojemności regałów: ",racks_capacities)
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gen_alg(number_of_generations, generation_size, mutation_prob, amount_of_promotion)
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print("pojemności regałów: ",racks_capacities)
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gen_alg(packs_sizes, racks_capacities, number_of_generations, generation_size, mutation_prob, amount_of_promotion)
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def run(self):
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while True:
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