79 lines
2.8 KiB
Python
79 lines
2.8 KiB
Python
from time import time as t
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import numpy as np
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import matplotlib.pyplot as plt
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from aco.ant_colony import ant_colony_opt
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class TSPSolver:
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def __init__(self, problem_instance):
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self.duration = np.inf
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self.found_length = np.inf
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self.solved = False
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self.problem_instance = problem_instance
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self.solution = None
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def compute_solution(self, verbose=True, return_value=True):
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self.solved = False
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if verbose:
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print(f"### solving with 'C++ ant colony optimization' ####")
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start_time = t()
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self.solution = ant_colony_opt(self.problem_instance)
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if not self.check_if_solution_is_valid():
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print(f"Error the solution of 'C++ ant colony optimization'"
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f" for problem {self.problem_instance.name} is not valid")
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if return_value:
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return False
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end_time = t()
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self.duration = np.around(end_time - start_time, 3)
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self.solved = True
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self.evaluate_solution()
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self._gap()
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if return_value:
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return self.solution
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def plot_solution(self):
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assert self.solved, "You can't plot the solution, you need to compute it first!"
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plt.figure(figsize=(8, 8))
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self._gap()
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plt.title(f"{self.problem_instance.name} solved with 'C++ ant colony optimization'"
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f" solver, gap {self.gap}")
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ordered_points = self.problem_instance.points[self.solution]
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plt.plot(ordered_points[:, 1], ordered_points[:, 2], 'b-')
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plt.show()
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def check_if_solution_is_valid(self):
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rights_values = np.sum(
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[self.check_validation(i, self.solution[:-1]) for i in
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np.arange(self.problem_instance.nPoints)])
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return rights_values == self.problem_instance.nPoints
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def check_validation(self, node, solution):
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if np.sum(solution == node) == 1:
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return 1
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else:
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return 0
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def evaluate_solution(self, return_value=False):
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total_length = 0
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starting_node = self.solution[0]
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from_node = starting_node
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for node in self.solution[1:]:
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total_length += self.problem_instance.dist_matrix[from_node, node]
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from_node = node
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self.found_length = total_length
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if return_value:
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return total_length
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def pass_and_check_if_solution_is_valid(self, solution):
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rights_values = np.sum(
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[self.check_validation(i, solution[:-1]) for i in
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np.arange(self.problem_instance.nPoints)])
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return rights_values == self.problem_instance.nPoints
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def _gap(self):
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self.evaluate_solution(return_value=False)
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self.gap = np.round(
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((self.found_length - self.problem_instance.best_sol) /
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self.problem_instance.best_sol) * 100, 2)
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