def random_neighbor(self, distance: int):
mask_list1 = General.get_mask_list(self.solution.n, distance, climb=False)
(value_list1, weight_list1) = General.parse_item_list_data(self.item_list)
solution_binary = "".join([str(item) for item in self.solution.item_list])
solution_number = int(solution_binary, 2)
mask_list = deepcopy(mask_list1)
random.shuffle(mask_list)
for mask in mask_list:
masked_number = solution_number ^ int(mask, 2)
masked_binary = bin(masked_number)[2:].zfill(self.solution.n)
neighbor = [int(digit) for digit in masked_binary]
neighbor_weight_list = [a*b for a,b in zip(neighbor, weight_list1)]
if sum(neighbor_weight_list) <= self.solution.capacity:
neighbor_value_list = [a*b for a,b in zip(neighbor, value_list1)]
self.solution.value = sum(neighbor_value_list)
self.solution.weight =sum(neighbor_weight_list)
self.solution.item_list = deepcopy(neighbor)
return True
return False
def run(self, tabu_list: list) -> None:
#print(f"ic| Executing Tabu Search with distance {self.distance}")
if self.counter == 0:
self.output_file.write_line(
self.output_filename.replace('TEMP-', ''))
self.output_file.write_line(str(self.solution.optimum))
self.output_file.write_line(
f"{self.counter} {self.solution.value}")
mask_list = General.get_mask_list(self.solution.n,
self.distance,
climb=True)
(value_list,
weight_list) = General.parse_item_list_data(self.item_list)
teste = self.evaluate_neighborhood_tabu(self.solution, mask_list,
value_list, weight_list,
tabu_list)
if teste:
self.counter += 1
#self.solution.print_solution()
#ic(f"{self.counter} {self.solution.value}")
self.output_file.write_line(
f"{self.counter} {self.solution.value}")
def run_vns(self, solution: Solution, item_list: list, max_iterations: int,
neighborhood_size: int, output_filename: str):
print(f"ic| run_vns: Executing Variable Neighbourhood Search")
counter = 0
output_file = FileWriter(file_name=output_filename)
output_file.write_line(output_filename.replace('TEMP-', ''))
output_file.write_line(str(solution.optimum))
output_file.write_line(f"{counter} {solution.value}")
(value_list,
weight_list) = General.parse_item_list_data(self.item_list)
for i in range(max_iterations):
initial_solution = solution
k = 1
while k <= neighborhood_size:
mask_list = General.get_mask_list(solution.n, k, climb=False)
initial_solution = self.random_neighbor(
solution, k, value_list, weight_list)
if not initial_solution:
continue
best_neighbor = self.evaluate_neighborhood(
initial_solution, mask_list, value_list, weight_list)
if best_neighbor and best_neighbor.value > solution.value:
counter += 1
solution = deepcopy(best_neighbor)
#solution.print_solution()
#ic(f"{counter} {solution.value}")
output_file.write_line(f"{counter} {solution.value}")
else:
k += 1
def run_local_search_improved(self):
print(
f"ic| run_local_search_improved: Executing Local Search with distance {self.distance}"
)
counter = 0
output_file = FileWriter(file_name=self.output_filename)
output_file.write_line(self.output_filename.replace('TEMP-', ''))
output_file.write_line(str(self.solution.optimum))
output_file.write_line(f"{counter} {self.solution.value}")
mask_list = General.get_mask_list(self.solution.n,
self.distance,
climb=True)
(value_list,
weight_list) = General.parse_item_list_data(self.item_list)
while self.evaluate_neighborhood_improved(self.solution, mask_list,
value_list, weight_list):
counter += 1
#self.solution.print_solution()
ic(f"{counter} {self.solution.value}")
output_file.write_line(f"{counter} {self.solution.value}")
# prevent looping indefinitely
if counter >= 100:
return
def run(self, counter):
#print(f"ic| Executing Iterated Local Search with distance {self.distance}")
if counter == 0:
self.output_file.write_line(
self.output_filename.replace('TEMP-', ''))
self.output_file.write_line(str(self.solution.optimum))
self.output_file.write_line(f"{counter} {self.solution.value}")
mask_list = General.get_mask_list(self.solution.n,
self.distance,
climb=True)
(value_list,
weight_list) = General.parse_item_list_data(self.item_list)
while self.evaluate_neighborhood(self.solution, mask_list, value_list,
weight_list):
#self.solution.print_solution()
#ic(f"{self.counter} {self.solution.value}")
pass
def run(self, counter):
#print(f"ic| Executing Multi Start Local Search with distance {self.distance}")
if counter == 0:
self.output_file.write_line(
self.output_filename.replace('TEMP-', ''))
self.output_file.write_line(str(self.solution.optimum))
self.output_file.write_line(f"{counter} {self.solution.value}")
mask_list = General.get_mask_list(self.solution.n,
self.distance,
climb=True)
(value_list,
weight_list) = General.parse_item_list_data(self.item_list)
counter_stop = 0
while self.evaluate_neighborhood(self.solution, mask_list, value_list,
weight_list):
counter_stop += 1
# prevent from looping indefinitely
if counter_stop >= 100:
return