import csv
import numpy as np
import TSP
# Define parameters
population_size = 10
temperature = 100000000
min_temperature = 0.00001
def update_temperature(temperature): return temperature * 0.999
# Read graph data
graph = TSP.csv_to_graph('state_distances.csv', 'New Mexico', 'New Mexico')
# Create population
population = [TSP.Individual(graph) for i in range(population_size)]
num_iter = 0
while temperature >= min_temperature:
for i in range(population_size):
population[i] = population[i].procreate(temperature)
temperature = update_temperature(temperature)
num_iter += 1
distances = [-individual.fitness for individual in population]
fittest_individual = population[np.argmin(distances)]
print('Shortest path: %.3f miles' % (-fittest_individual.fitness))
print('Shortest path: ' + ','.join(fittest_individual.path))
# Define parameters
population_size = 1
temperature = 1000000
min_temperature = 0.0001
def update_temperature(temperature):
return temperature * 0.999
# Read graph data
graph = TSP.csv_to_graph('state_distances.csv', 'New Mexico', 'New Mexico')
# Create population
individual = TSP.Individual(graph)
out_file = open('shortest_path_iterative.csv', 'w')
out_csv = csv.writer(out_file, lineterminator='\n')
out_csv.writerow(['cumulative.distance', 'name', 'iteration'])
num_iter = 0
while temperature >= min_temperature:
for i in range(population_size):
individual = individual.procreate(temperature)
temperature = update_temperature(temperature)
cumulative_distance = 0
old_state = individual.path[0]
for state in individual.path:
