def load_solution(self, path=None):
"""
:param str path: (Optional) Path of solution file. If solution not in directory containing problem file.
:return: A solution object.
:rtype: tsp.models.Solution
"""
load_path = self.out_abs_path if path is None else path
if self._solution is None:
self._solution = tsp.load_solution(load_path)
return self._solution
plt.title(title)
plt.savefig(savename)
plt.cla()
if __name__ == "__main__":
DATA_PATH = Path("data")
assert DATA_PATH.exists()
NAME = "berlin52"
NAME = "ulysses16"
PROBLEM_PATH = DATA_PATH / f"{NAME}.tsp"
SOLUTION_PATH = DATA_PATH / f"{NAME}.opt.tour"
p = tsp.load_problem(PROBLEM_PATH)
s = tsp.load_solution(SOLUTION_PATH)
print(f"Optimal tour: {s.tours}")
print(f"Optimal tour: {p.trace_tours(s)}")
g = p.get_graph()
draw(g, s.tours[0])
#def get_colors(
# graph, edges, default='b', highlight='r',
#):
# c = np.full(len(graph.edges), default)
# w = np.ones(len(graph.edges))
#
# for i, edge in enumerate(graph.edges):
# if edge in edges:
# c[i] = highlight
def parse_solution(res_path):
""" Парсим файл решения """
solution = tsplib95.load_solution(res_path)
return np.array(solution.tours[0])
import acopy
import tsplib95
colony = acopy.ant.Colony(alpha=1, beta=3)
problem = tsplib95.load_problem('./data/att48.tsp')
G = problem.get_graph()
solution = tsplib95.load_solution('./data/att48.opt.tour')
print(problem.trace_tours(solution))
ants = 25
iterations = 100
deviation1 = []
solver = acopy.solvers.Solver(rho=0.2, q=1)
for i in range(0, 9):
G = problem.get_graph()
colony = acopy.ant.Colony(alpha=1, beta=2)
tour = solver.solve(G, colony, ants, iterations)
print(tour.cost)
deviation1.append(abs(33522 - tour.cost))
result1 = sum(deviation1) / len(deviation1)
print("Ants: ", ants, "Iterations: ", iterations)
print("Averave deviation: ", result1)
print("nodes: ", tour.nodes)
print("\n")
################################################################################
import acopy
import tsplib95
from functions import distance_on_unit_sphere
from plotSprawko import plot
colony = acopy.ant.Colony(alpha=1, beta=3)
problem = tsplib95.load_problem('./data/ulysses16.tsp',
distance_on_unit_sphere)
problem.special = distance_on_unit_sphere
G = problem.get_graph()
solution = tsplib95.load_solution('./data/ulysses16.opt.tour')
# problem.trace_tours(solution)
ants = 25
iterations = 100
deviation1 = []
solver = acopy.solvers.Solver(rho=0.03, q=1)
optimal_solution = 6859
for i in range(0, 9):
G = problem.get_graph()
colony = acopy.ant.Colony(alpha=1, beta=3)
tour = solver.solve(G, colony, ants, iterations)
print(tour.cost)
deviation1.append(abs(optimal_solution - tour.cost))
result1 = sum(deviation1) / len(deviation1)
import acopy
import tsplib95
colony = acopy.ant.Colony(alpha=1, beta=3)
problem = tsplib95.load_problem('./data/eil51.tsp')
G = problem.get_graph()
solution = tsplib95.load_solution('./data/eil101.opt.tour')
# print(problem.trace_tours(solution))
# optimal : 426
ants = 200
iterations = 100
deviation1 = []
solver = acopy.solvers.Solver(rho=0.2, q=1)
for i in range(0, 9):
G = problem.get_graph()
colony = acopy.ant.Colony(alpha=1, beta=2)
tour = solver.solve(G, colony, ants, iterations)
print(tour.cost)
deviation1.append(abs(426-tour.cost))
result1 = sum(deviation1) / len(deviation1)
print("Ants: ", ants, "Iterations: ", iterations)
print("Averave deviation: ", result1)
print("nodes: ", tour.nodes)
print("\n")