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solver.py
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solver.py
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import json
import random
from termcolor import colored
import matplotlib.pyplot as plt
class TabuSearchSolver(object):
def __init__(self, data_path: str, taboo_list_size, seed=None) -> None:
self.data = self.__load_data(data_path)
if seed:
self.seed = seed
else:
self.seed = random.randint(0, 10000)
self.initial_solution = self.__create_initial_solution(
self.data, seed=seed, show=True)
self.current_solution = self.initial_solution.copy()
self.best_solution = self.initial_solution.copy()
self.best_solution_value = self.__calculate_value()
self.taboo_list = []
self.taboo_list_size = taboo_list_size
def __load_data(self, json_path: str):
"""
Loads data from a JSON file.
"""
with open(json_path, "r") as f:
data = json.load(f)
try:
data = {int(key): {int(key2): value2 for key2, value2 in value.items()}
for key, value in data.items()}
except:
pass
return data
def __swap(self, solution: list, i, j) -> list:
"""
Takes a list and swaps the elements at index i and j.
"""
solution_copy = solution.copy()
solution_copy[i], solution_copy[j] = solution_copy[j], solution_copy[i]
return solution_copy
def __insertion(self, solution: list, node_idx: int) -> list:
"""
Inserts a node into a solution.
"""
solution_copy = solution.copy()
solution_copy.insert(node_idx, node_idx)
return solution_copy
def __create_initial_solution(self, instances: dict, seed: int, show: bool = False) -> list:
"""
Creates a random initial solution.
"""
n_jobs = len(instances)
initial_solution = list(range(1, n_jobs + 1))
random.seed(seed)
random.shuffle(initial_solution)
if show:
print("Initial solution:", initial_solution)
return initial_solution
def __calculate_value(self):
"""
Calculates the value of the current solution.
"""
value = 0
for first_node_idx in range(len(self.current_solution) - 1):
first_node = self.current_solution[first_node_idx]
second_node = self.current_solution[first_node_idx + 1]
lowest_node = min(first_node, second_node)
highest_node = max(first_node, second_node)
temp_value = self.data[lowest_node][highest_node]
value += temp_value
return value
def __calculate_neighbor_value(self, neighbor: list) -> int:
"""
Calculates the value of a neighbor.
"""
neighbor_value = 0
for first_node_idx in range(len(neighbor) - 1):
first_node = neighbor[first_node_idx]
second_node = neighbor[first_node_idx + 1]
lowest_node = min(first_node, second_node)
highest_node = max(first_node, second_node)
if lowest_node == highest_node:
continue
temp_value = self.data[lowest_node][highest_node]
neighbor_value += temp_value
return_cost = self.data[min(
neighbor[0], neighbor[-1])][max(neighbor[0], neighbor[-1])]
return neighbor_value + return_cost
def __is_taboo(self, node_idx: int) -> bool:
"""
Checks if a node is taboo.
"""
return node_idx in self.taboo_list
def __is_solution_better(self, solution: list) -> bool:
"""
Checks if a solution is better than the current best solution.
"""
solution_value = self.__calculate_neighbor_value(solution)
if solution_value < self.best_solution_value:
return True
return False
def __generate_neighbors(self, solution: list) -> list:
"""
Generates neighbors of a solution. with swap and insertion
"""
neighbors = []
for i in range(len(solution) - 1):
for j in range(i + 1, len(solution)):
neighbor = self.__swap(solution, i, j)
if not self.__is_taboo(neighbor):
neighbors.append(neighbor)
return neighbors
def __get_best_neighbor(self, neighbors: list) -> list:
"""
Returns the best neighbor of a solution.
"""
best_neighbor = None
best_neighbor_value = float("inf")
for neighbor in neighbors:
neighbor_value = self.__calculate_neighbor_value(neighbor)
if neighbor_value < best_neighbor_value:
best_neighbor = neighbor
best_neighbor_value = neighbor_value
return best_neighbor
def solve(self, iteration: int) -> list:
"""
Solves the TSP problem using Taboo Search.
"""
for i in range(iteration):
neighbors = self.__generate_neighbors(self.current_solution)
# print(colored("Iteration:", "green"), i)
best_neighbor = self.__get_best_neighbor(neighbors)
if self.__is_solution_better(best_neighbor):
self.current_solution = best_neighbor
self.best_solution = best_neighbor
self.best_solution_value = self.__calculate_neighbor_value(
best_neighbor)
temp_neighbor = best_neighbor.copy()
temp_neighbor.append(temp_neighbor[0])
print("Iteration:", i, "Best neighbor:", temp_neighbor,
"Value:", self.best_solution_value)
self.taboo_list.append(self.current_solution[0])
if len(self.taboo_list) > self.taboo_list_size:
self.taboo_list.pop(0)
return self.best_solution
def show_results_as_graph(self):
"""
Show cost of neighbors at each iteration as graph.
"""
neighbors = self.__generate_neighbors(self.current_solution)
values = []
for neighbor in neighbors:
values.append(self.__calculate_neighbor_value(neighbor))
plt.plot(values)
plt.show()