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This commit is contained in:
UmbertoJr 2019-11-18 08:21:05 +01:00
parent d29a162276
commit 2e9910d324
3 changed files with 73 additions and 61 deletions
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1
run.py
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@ -23,7 +23,6 @@ def run(show_plots=False, verbose=False):
solver = Solver_TSP(init)
for improve in improvements:
solver.bind(improve)
end - start
solver(instance, return_value=False, verbose=verbose)
if verbose:

1
src/TSP_solver.py
View file

@ -46,6 +46,7 @@ class Solver_TSP:
print("init ok")
for i in range(1, len(self.methods)):
self.solution = self.available_improvements[self.methods[i]](self.solution, self.instance)
print(len(self.solution))
assert self.check_if_solution_is_valid(self.solution), "Error the solution is not valid"
print("improve ok")

132
src/local_search.py
View file

@ -1,5 +1,6 @@
import os
import numpy as np
if 'AI' in os.getcwd():
from src.utils import *
else:
@ -65,86 +66,97 @@ class TwoOpt:
else:
return new_tsp_sequence.tolist(), new_len, uncross
return new_tsp_sequence.tolist(), new_len, uncross
# return new_tsp_sequence.tolist(), new_len, uncross
return new_tsp_sequence.tolist()
class TwoDotFiveOpt:
@staticmethod
def step2dot5opt(solution, matrix_dist, distance):
"""
@staticmethod
def step2dot5opt(solution, matrix_dist, distance):
"""
One step of 2opt, one double loop and return first improved sequence
@param solution:
@param matrix_dist:
@param distance:
@return:
"""
seq_length = len(solution) - 2
tsp_sequence = np.array(solution)
uncrosses = 0
for i in range(1, seq_length - 1):
for j in range(i + 1, seq_length):
# 2opt swap
twoOpt_tsp_sequence = TwoOpt.swap2opt(tsp_sequence, i, j)
twoOpt_len = distance + TwoOpt.gain(i, j , tsp_sequence, matrix_dist)
# node shift 1
first_shift_tsp_sequence = TwoDotFiveOpt.shift1(tsp_sequence, i,j)
first_shift_len = distance + TwoDotFiveOpt.shift_gain1(i,j, tsp_sequence, matrix_dist)
# node shift 2
second_shift_tsp_sequence = TwoDotFiveOpt.shift2(tsp_sequence, i,j)
second_shift_len = distance + TwoDotFiveOpt.shift_gain2(i,j, tsp_sequence, matrix_dist)
seq_length = len(solution) - 2
tsp_sequence = np.array(solution)
uncrosses = 0
for i in range(1, seq_length - 1):
for j in range(i + 1, seq_length):
# 2opt swap
twoOpt_tsp_sequence = TwoOpt.swap2opt(tsp_sequence, i, j)
twoOpt_len = distance + TwoOpt.gain(i, j, tsp_sequence, matrix_dist)
# node shift 1
first_shift_tsp_sequence = TwoDotFiveOpt.shift1(tsp_sequence, i, j)
first_shift_len = distance + TwoDotFiveOpt.shift_gain1(i, j, tsp_sequence, matrix_dist)
# node shift 2
second_shift_tsp_sequence = TwoDotFiveOpt.shift2(tsp_sequence, i, j)
second_shift_len = distance + TwoDotFiveOpt.shift_gain2(i, j, tsp_sequence, matrix_dist)
best_len, best_method = min([twoOpt_len, first_shift_len, second_shift_len]), np.argmin([twoOpt_len, first_shift_len, second_shift_len])
sequences = [twoOpt_tsp_sequence, first_shift_tsp_sequence, second_shift_tsp_sequence]
if best_len < distance:
uncrosses += 1
tsp_sequence = sequences[best_method]
distance = best_len
# print(distance, best_method, [twoOpt_len, first_shift_len, second_shift_len])
return tsp_sequence, distance, uncrosses
best_len, best_method = min([twoOpt_len, first_shift_len, second_shift_len]), np.argmin(
[twoOpt_len, first_shift_len, second_shift_len])
sequences = [twoOpt_tsp_sequence, first_shift_tsp_sequence, second_shift_tsp_sequence]
if best_len < distance:
uncrosses += 1
tsp_sequence = sequences[best_method]
distance = best_len
# print(distance, best_method, [twoOpt_len, first_shift_len, second_shift_len])
return tsp_sequence, distance, uncrosses
@staticmethod
def shift1(tsp_sequence, i, j):
new_tsp_sequence = np.concatenate([tsp_sequence[:i], tsp_sequence[i+1: j+1], [tsp_sequence[i]], tsp_sequence[j+1:]])
return new_tsp_sequence
@staticmethod
def shift1(tsp_sequence, i, j):
new_tsp_sequence = np.concatenate(
[tsp_sequence[:i], tsp_sequence[i + 1: j + 1], [tsp_sequence[i]], tsp_sequence[j + 1:]])
return new_tsp_sequence
@staticmethod
def shift_gain1(i, j , tsp_sequence, matrix_dist):
old_link_len = (matrix_dist[tsp_sequence[i], tsp_sequence[i - 1]]+ matrix_dist[tsp_sequence[i], tsp_sequence[i + 1]] + matrix_dist[tsp_sequence[j], tsp_sequence[j + 1]])
changed_links_len = (matrix_dist[tsp_sequence[i - 1], tsp_sequence[i + 1]] + matrix_dist[tsp_sequence[i], tsp_sequence[j]] + matrix_dist[tsp_sequence[i], tsp_sequence[j + 1]])
return - old_link_len + changed_links_len
@staticmethod
def shift_gain1(i, j, tsp_sequence, matrix_dist):
old_link_len = (matrix_dist[tsp_sequence[i], tsp_sequence[i - 1]] + matrix_dist[
tsp_sequence[i], tsp_sequence[i + 1]] + matrix_dist[tsp_sequence[j], tsp_sequence[j + 1]])
changed_links_len = (matrix_dist[tsp_sequence[i - 1], tsp_sequence[i + 1]] + matrix_dist[
tsp_sequence[i], tsp_sequence[j]] + matrix_dist[tsp_sequence[i], tsp_sequence[j + 1]])
return - old_link_len + changed_links_len
@staticmethod
def shift2(tsp_sequence, i, j):
new_tsp_sequence = np.concatenate([tsp_sequence[:i], [tsp_sequence[j]], tsp_sequence[i: j], tsp_sequence[j+1:]])
return new_tsp_sequence
@staticmethod
def shift2(tsp_sequence, i, j):
new_tsp_sequence = np.concatenate(
[tsp_sequence[:i], [tsp_sequence[j]], tsp_sequence[i: j], tsp_sequence[j + 1:]])
return new_tsp_sequence
@staticmethod
def shift_gain2(i, j , tsp_sequence, matrix_dist):
old_link_len = (matrix_dist[tsp_sequence[i], tsp_sequence[i - 1]] + matrix_dist[tsp_sequence[j], tsp_sequence[j - 1]] + matrix_dist[tsp_sequence[j], tsp_sequence[j + 1]])
changed_links_len = (matrix_dist[tsp_sequence[j], tsp_sequence[i - 1]] + matrix_dist[tsp_sequence[i], tsp_sequence[j]] + matrix_dist[tsp_sequence[j - 1], tsp_sequence[j + 1]])
return - old_link_len + changed_links_len
@staticmethod
def shift_gain2(i, j, tsp_sequence, matrix_dist):
old_link_len = (matrix_dist[tsp_sequence[i], tsp_sequence[i - 1]] + matrix_dist[
tsp_sequence[j], tsp_sequence[j - 1]] + matrix_dist[tsp_sequence[j], tsp_sequence[j + 1]])
changed_links_len = (
matrix_dist[tsp_sequence[j], tsp_sequence[i - 1]] + matrix_dist[tsp_sequence[i], tsp_sequence[j]] +
matrix_dist[tsp_sequence[j - 1], tsp_sequence[j + 1]])
return - old_link_len + changed_links_len
@staticmethod
def loop2dot5opt(solution, instance, max_num_of_changes=400): # Iterate stoep2opt max_iter times (2-opt local search)
"""
@staticmethod
def loop2dot5opt(solution, instance,
max_num_of_changes=400): # Iterate stoep2opt max_iter times (2-opt local search)
"""
@param solution:
@param instance:
@param max_num_of_changes:
@return:
"""
matrix_dist = instance.dist_matrix
actual_len = compute_lenght(solution, matrix_dist)
new_tsp_sequence = np.copy(np.array(solution))
uncross = 0
while uncross < max_num_of_changes:
new_tsp_sequence, new_len, uncr_ = TwoDotFiveOpt.step2dot5opt(new_tsp_sequence, matrix_dist, actual_len)
uncross += uncr_
# print(new_len, uncross)
if new_len < actual_len:
actual_len = new_len
else:
return new_tsp_sequence.tolist(), actual_len, uncross
return new_tsp_sequence.tolist(), actual_len, uncross
matrix_dist = instance.dist_matrix
actual_len = compute_lenght(solution, matrix_dist)
new_tsp_sequence = np.copy(np.array(solution))
uncross = 0
while uncross < max_num_of_changes:
new_tsp_sequence, new_len, uncr_ = TwoDotFiveOpt.step2dot5opt(new_tsp_sequence, matrix_dist, actual_len)
uncross += uncr_
# print(new_len, uncross)
if new_len < actual_len:
actual_len = new_len
else:
return new_tsp_sequence.tolist(), actual_len, uncross
# return new_tsp_sequence.tolist(), actual_len, uncross
return new_tsp_sequence.tolist()