max_edges=params_dict['poligonProblem.max_edges'],
vns_vnd=params_dict['poligonProblem.vns_vnd'])
while initial_solution is None or len(initial_solution) < params_dict['initialSolution.lenght']:
problem.num_shapes = i
# una posible mejora podria ser optimizar los polígonos que están introduciendo más error
# i-1 optimiza el último poligono introducido
problem.polygon_list = [i-1]
if not initial_solution is None:
initial_solution.append([problem.get_random_polygon(), problem.get_random_color()])
#Ejecuta busqueda tabú
searcher = TabuSearch(problem,
max_iterations=params_dict['initialTabuSearch.max_iterations'],
list_length=params_dict['initialTabuSearch.list_length'],
improved_event=improve,
tolerance=params_dict['initialTabuSearch.tolerance'])
searcher.search(initial_solution=initial_solution)
# Comprueba si la solucion actual es mejor que la encontrada
if current_fitness > searcher.best_fitness or initial_solution is None:
initial_solution = searcher.best
current_fitness = searcher.best_fitness
# aqui ya tenemos 2 vecinos
if i > 1:
# Indices de los poligonos que quiero optimizar
# Con None le indico que lo aplique a todos los poligonos
problem.polygon_list=None
from optimization.tabu import TabuSearch
from problems.tsp.tsp_problem import TSPProblem
from problems.tsp.tsplib import get_cities_tup
if __name__ == '__main__':
def improve(caller):
current = [cities[i] for i in caller.best] + [cities[caller.best[0]]]
problem.plot_cities(current)
cities = get_cities_tup(file='berlin52.tsp')
problem = TSPProblem(cities, candidates_by_iteration=200, neighborhood=2)
searcher = TabuSearch(problem,
max_iterations=1000,
list_length=10,
improved_event=improve)
searcher.search()
best = [cities[i] for i in searcher.best] + [cities[searcher.best[0]]]
problem.plot_cities(best)
current_fitness = 100
problem = TrianglesProblem(img,
num_shapes=i,
candidates_by_iteration=1000,
triangle_list=[i - 1],
sol_file='data/images/little_mondrian_sol.png')
while len(initial_solucion) < 50:
problem.num_shapes = i
problem.triangle_list = [i - 1]
initial_solucion.append(
[problem.get_random_triangle(),
problem.get_random_color()])
searcher = TabuSearch(problem,
max_iterations=1000,
list_length=10,
improved_event=improve,
tolerance=100)
searcher.search(initial_solution=initial_solucion)
if current_fitness > searcher.best_fitness:
initial_solucion = searcher.best
current_fitness = searcher.best_fitness
i += 1
print("Solution length: %d" % i)
else:
initial_solucion = initial_solucion[:-1]
print("num fitness per second: %f" %
(problem.fitness_count / problem.fitness_time))
return sol
def perturb(self, sol):
sol = copy(sol)
sol = self.local_search.problem.swap(sol, self.perturbation_level)
return sol
if __name__ == '__main__':
def improve(caller):
current = [cities[i] for i in caller.best] + [cities[caller.best[0]]]
problem.plot_cities(current)
cities = get_cities_tup(file='berlin52.tsp')
problem = TSPProblem(cities, candidates_by_iteration=200, neighborhood=2)
searcher = TabuSearch(problem,
max_iterations=100,
list_length=10,
verbose=True,
improved_event=improve)
ils = ILSTSP(searcher, max_iterations=10, perturbation_level=2)
ils.search()
best = [cities[i] for i in searcher.best] + [cities[searcher.best[0]]]
problem.plot_cities(best)
print("Best score: %f" % problem.best_fitness)
print("Finish")