From 2e9910d324d24cba0ea37ac8dfcaff1164d74bcf Mon Sep 17 00:00:00 2001 From: UmbertoJr Date: Mon, 18 Nov 2019 08:21:05 +0100 Subject: [PATCH] local --- run.py | 1 - src/TSP_solver.py | 1 + src/local_search.py | 132 ++++++++++++++++++++++++-------------------- 3 files changed, 73 insertions(+), 61 deletions(-) diff --git a/run.py b/run.py index 83487d7..54c223a 100644 --- a/run.py +++ b/run.py @@ -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: diff --git a/src/TSP_solver.py b/src/TSP_solver.py index 931141c..f9f332c 100644 --- a/src/TSP_solver.py +++ b/src/TSP_solver.py @@ -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") diff --git a/src/local_search.py b/src/local_search.py index 7399f6d..f384e95 100644 --- a/src/local_search.py +++ b/src/local_search.py @@ -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()