def runAntSystem(pop_multiplier, alpha, beta, evaporation_rate, execution):
dist = readDistFile(instance)
ant_system = AntSystem(dist, len(dist) * pop_multiplier, ITERATIONS, alpha, beta, evaporation_rate, Q)
ant_system.initPheromone()
ant_system.initAnts()
for t in range(ant_system.iterations):
best_evaluation_iter = float('inf')
for ant in ant_system.population:
while (len(ant.solution) < ant_system.node_count):
ant_system.goToNextNode(ant)
ant.solution.append(ant.solution[0])
ant.evaluation = ant_system.calculateFO(ant.solution)
if (ant.evaluation < best_evaluation_iter):
best_evaluation_iter = ant.evaluation
if (ant.evaluation < ant_system.best_evaluation):
ant_system.best_solution = ant.solution
ant_system.best_evaluation = ant.evaluation
fileOperations(best_evaluation_iter, pop_multiplier, alpha, beta, evaporation_rate, execution, 0)
ant_system.pheromoneUpdate()
ant_system.restartAnts()
fileOperations(ant_system.best_evaluation, pop_multiplier, alpha, beta, evaporation_rate, execution, 1)
def solve_it(input_data, method="", oporation="", solo=False, default=True):
# Modify this code to run your optimization algorithm
# parse the input
lines = input_data.split('\n')
nodeCount = int(lines[0])
points = []
for i in range(1, nodeCount + 1):
line = lines[i]
parts = line.split()
points.append(Point(float(parts[0]), float(parts[1])))
if len(points) > 30000 and default:
print("greedy")
return greedy(points)
NPproblem_tsp = tsp.tsp(points)
if default:
if len(points) > 200:
approx = NPproblem_tsp.christofides()
return print_solution(NPproblem_tsp, approx, "simulatedAnneling")
else:
approx = NPproblem_tsp.gurobi_method()
return approx
if method == 'approximation2':
approx = NPproblem_tsp.approximation2()
elif method == 'christofides':
approx = NPproblem_tsp.christofides()
elif method == 'antSystem':
Hormigas = AntSystem.AntSystem(points, 1, 2, 0.02, 10, nodeCount,
nodeCount)
algo = Hormigas.calcule_route()
return verbose(algo[1], algo[0])
elif method == 'gurobi':
approx = NPproblem_tsp.gurobi_method()
return approx
else:
approx = NPproblem_tsp.christofides()
if solo:
return verbose(approx[1], approx[0])
else:
return print_solution(NPproblem_tsp, approx, oporation)
from sys import argv
from utils import *
from constants import *
from AntSystem import *
instanceFile = 'LAU15.txt' if len(argv) < 2 else argv[1]
dist = readDistFile(instanceFile)
ant_system = AntSystem(dist,
len(dist) * POP_MULTIPLIER, ITERATIONS, ALPHA, BETA,
EVAPORATION_RATE, Q)
ant_system.initPheromone()
ant_system.initAnts()
for t in range(ant_system.iterations):
best_evaluation_iter = float('inf')
for ant in ant_system.population:
while (len(ant.solution) < ant_system.node_count):
ant_system.goToNextNode(ant)
ant.solution.append(ant.solution[0])
ant.evaluation = ant_system.calculateFO(ant.solution)
if (ant.evaluation < best_evaluation_iter):
best_evaluation_iter = ant.evaluation
if (ant.evaluation < ant_system.best_evaluation):
ant_system.best_solution = ant.solution
ant_system.best_evaluation = ant.evaluation
print(f'Best evaluation of iteration {t}: {best_evaluation_iter}')
