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On-the-influence-of-using-initialization-functions-on-genetic-algorithms-solving-combinatorial-optim
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initialize.py
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initialize.py
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from nn import NearestNeighbour
from insertion import Insertion
from i1 import I1
from hfunctions import HelpfulFunctions
from cities import cities
import numpy as np
from tabulate import tabulate
import time
import ray
import winsound
ray.shutdown()
ray.init()
cities = cities.cities
city_names = cities.city_names
heuristic_names = ["NN", "In", "I1"]
population_size = 48
nr_of_experiments = 5
heuristics = [NearestNeighbour, Insertion, I1]
randomnesses = [1, 0.5, 0.1, 0]
results = np.load('data.npy')
times_of_execution = np.load('times.npy')
if len(results) == 0:
results = np.zeros((len(heuristics), len(randomnesses), len(cities), 50))
times_of_execution = np.zeros((len(heuristics), len(randomnesses), len(cities), 50))
average_fitness_arr = np.zeros((len(heuristics), len(randomnesses), len(cities)), dtype=float)
fitnessstdev_arr = np.zeros((len(heuristics), len(randomnesses), len(cities)), dtype=float)
timestdev_arr = np.zeros((len(heuristics), len(randomnesses), len(cities)), dtype=float)
average_times = np.zeros((len(heuristics), len(randomnesses), len(cities)), dtype=float)
def reset_saved_data():
results = np.zeros((len(heuristics), len(randomnesses), len(cities), 50))
times_of_execution = np.zeros((len(heuristics), len(randomnesses), len(cities), 50))
np.save('data', results)
np.save('times', times_of_execution)
@ray.remote
def itera(h, population_size, city, randomness, indeks_grada, broj_eksperimenta, counter):
time_at_start = time.time()
population, population_fitness = HelpfulFunctions.makePopulation(heuristics[h], population_size, city, randomness)
best_solution = HelpfulFunctions.algorithm(population, population_fitness, population_size, city_names[indeks_grada] + ":" + str(randomness) + ":" + heuristic_names[h])
time_of_execution = time.time() - time_at_start
rjesenje = int(HelpfulFunctions.evaluate(best_solution))
print("Tsp problem: " + heuristic_names[h] + ":" + str(randomness*100) + "%:" + city_names[indeks_grada] + ":" + str(broj_eksperimenta) + " (" + str(counter) + "/50)")
print("Time: {0:.0f} seconds".format(time_of_execution))
print("Fitness: " + str(rjesenje))
return (time_of_execution, rjesenje)
while True:
counter = 0
max_counter = 50
tasks = []
task_settings = []
for h, heur in enumerate(heuristics):
for r, randomness in enumerate(randomnesses):
if r == 3 and h != 0:
continue
for g, city in enumerate(cities):
for i in range(nr_of_experiments):
if abs(results[h,r,g,i]) < 0.1:
if counter >= max_counter:
break
tasks.append(itera.remote(h, population_size, city, randomness, g, i, counter))
task_settings.append((h,r,g,i))
counter += 1
print("Added to execution queue: " + heuristic_names[h] + ":" + str(randomness*100) + "%:" + city_names[g] + ":" + str(i) + " (" + str(counter) + "/50)")
if counter >= max_counter:
break
if counter >= max_counter:
break
if counter >= max_counter:
break
if counter > 0:
ray_results = ray.get(tasks)
for i, p in enumerate(task_settings):
results[p[0],p[1],p[2],p[3]] = float(ray_results[i][1])
times_of_execution[p[0],p[1],p[2],p[3]] = float(ray_results[i][0])
print("Saving results...")
np.save('data', results)
np.save('data_backup', results)
np.save('times', times_of_execution)
np.save('times_backup', times_of_execution)
print("Results saved!")
frequency = 2500 # Set Frequency To 2500 Hertz
duration = 1000 # Set Duration To 1000 ms == 1 second
winsound.Beep(frequency, duration)
else:
break
def showResults():
for h, heur in enumerate(heuristics):
print("Heuristic: " + heuristic_names[h])
for r, randomness in enumerate(randomnesses):
not_empty = False
for g, city in enumerate(cities):
average_fitness = 0
average_time = 0
for i in range(nr_of_experiments):
if abs(results[h,r,g,i]) > 0.1:
not_empty = True
average_fitness += results[h,r,g,i]
average_time += times_of_execution[h,r,g,i]
average_fitness /= nr_of_experiments
average_fitness_arr[h,r,g] = average_fitness
average_time /= nr_of_experiments
average_times[h,r,g] = average_time
fitnessstdev_arr[h,r,g] = np.std(results[h,r,g,0:nr_of_experiments])
timestdev_arr[h,r,g] = np.std(times_of_execution[h,r,g,0:nr_of_experiments])
if not_empty:
info = {'city': city_names, 'Avg. fitness': average_fitness_arr[h,r], 'St. dev. of fitness': fitnessstdev_arr[h,r], 'Avg. time': average_times[h,r], 'St. dev. of time': timestdev_arr[h,r]}
print("Randomness: " + str(randomness*100) + "%")
print(tabulate(info, headers='keys', tablefmt='fancy_grid'))
showResults()