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TabuSearch.py
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TabuSearch.py
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from testParameters import *
import random
from random import shuffle
import time
class Heuristic:
@staticmethod
def apply(item, bins):
return bins
class FirstFit(Heuristic):
@staticmethod
def apply(item, bins):
b = next((b for b in bins if b.can_add_item(item)), None)
if not b:
b = Bin(bins[0].capacity)
bins.append(b)
b.add_item(item)
return bins
class BestFit(Heuristic):
@staticmethod
def apply(item, bins):
valid_bins = (b for b in bins if b.can_add_item(item))
sorted_bins = sorted(valid_bins, key=lambda x: x.filled_space(), reverse=True)
if sorted_bins:
b = sorted_bins[0]
else:
b = Bin(bins[0].capacity)
bins.append(b)
b.add_item(item)
return bins
class NextFit(Heuristic):
@staticmethod
def apply(item, bins):
b = bins[-1]
if not b.add_item(item):
b = Bin(bins[0].capacity)
bins.append(b)
b.add_item(item)
return bins
class WorstFit(Heuristic):
@staticmethod
def apply(item, bins):
valid_bins = (b for b in bins if b.can_add_item(item))
sorted_bins = sorted(valid_bins, key=lambda x: x.filled_space())
if sorted_bins:
b = sorted_bins[0]
else:
b = Bin(bins[0].capacity)
bins.append(b)
b.add_item(item)
return bins
class Bin:
def __init__(self, capacity):
self.capacity = capacity
self.items = []
def add_item(self, new_item):
if self.can_add_item(new_item):
self.items.append(new_item)
return True
return False
def can_add_item(self, new_item):
return new_item.size <= self.open_space()
def filled_space(self):
return sum(item.size for item in self.items)
def open_space(self):
return self.capacity - self.filled_space()
def fitness(self):
return (self.filled_space() / self.capacity) ** 2
def showBinContent(self):
for singleItem in self.items:
singleItem.showItemSize()
class MoveOperator:
@staticmethod
def apply(items, choices):
return items
class Remove(MoveOperator):
@staticmethod
def apply(items, choices):
num_removals = random.randrange(len(items))
for _ in range(num_removals):
to_remove = random.randrange(len(items))
items = items[:to_remove] + items[to_remove + 1:]
return items
class Add(MoveOperator):
@staticmethod
def apply(items, choices):
num_inserts = random.randrange(len(items) + 1)
for _ in range(num_inserts):
to_insert = random.randrange(len(items))
items = items[:to_insert] + random.choice(choices) + items[to_insert:]
return items
class Change(MoveOperator):
@staticmethod
def apply(items, choices):
num_changes = random.randrange(len(items)+1)
items = list(items)
for _ in range(num_changes):
to_change = random.randrange(len(items))
items[to_change] = random.choice(choices)
return "".join(items)
class Swap(MoveOperator):
@staticmethod
def apply(items, choices):
num_swaps = random.randrange(len(items))
items = list(items)
for _ in range(num_swaps):
idx1, idx2 = random.randrange(len(items)), random.randrange(len(items))
items[idx1], items[idx2] = items[idx2], items[idx1]
return "".join(items)
class Item:
def __init__(self, size):
self.size = size
def showItemSize(self):
print(self.size)
class TabuSearch:
heuristic_map = {
"f": FirstFit,
"n": NextFit,
"w": WorstFit,
"b": BestFit,
}
movers = [Add, Change, Remove, Swap]
def __init__(self, capacity, items, MAX_COMBINATION_LENGTH=10, MAX_ITERATIONS=5000, MAX_NO_CHANGE = 1000):
"""
Creates an instance that can run the tabu search algorithm.
:param capacity: The capacity of a bin.
:param items: The items that have to be packed in bins.
"""
self.MAX_COMBINATION_LENGTH = MAX_COMBINATION_LENGTH
self.MAX_ITERATIONS = MAX_ITERATIONS
self.MAX_NO_CHANGE = MAX_NO_CHANGE
self.bin_capacity = capacity
self.items = items
self.fitness = 0
self.bins = [Bin(capacity)]
self.tabu_list = set()
def run(self):
"""
Runs the tabu search algorithm and returns the results at the end of the process.
:return: (num_iterations, num_no_changes, chosen_combination)
"""
combination = "".join(
[random.choice(list(self.heuristic_map.keys())) for _ in range(random.randrange(self.MAX_COMBINATION_LENGTH) or 1)])
self.bins = self.generate_solution(combination)
self.fitness = sum(b.fitness() for b in self.bins) / len(self.bins)
self.tabu_list.add(combination)
current_iteration = 0
num_no_change = 0
while num_no_change < self.MAX_NO_CHANGE and current_iteration < self.MAX_ITERATIONS:
new_combination = self.apply_move_operator(combination)
while len(new_combination) > self.MAX_COMBINATION_LENGTH :
new_combination = self.apply_move_operator(new_combination)
if new_combination not in self.tabu_list:
self.tabu_list.add(new_combination)
solution = self.generate_solution(new_combination)
fitness = sum(b.fitness() for b in solution) / len(solution)
if fitness > self.fitness:
self.bins = solution
self.fitness = fitness
num_no_change = 0
combination = new_combination
current_iteration += 1
num_no_change += 1
return current_iteration, num_no_change, combination
def run2(self,AGsol):
"""
Runs the tabu search algorithm and returns the results at the end of the process.
:return: (num_iterations, num_no_changes, chosen_combination)
"""
combination = AGsol.best_solution.pattern
self.bins =self.generate_solution(combination)
self.fitness = AGsol.best_solution.fitness
self.tabu_list.add(combination)
current_iteration = 0
num_no_change = 0
while num_no_change < self.MAX_NO_CHANGE and current_iteration < self.MAX_ITERATIONS:
new_combination = self.apply_move_operator(combination)
while len(new_combination) > self.MAX_COMBINATION_LENGTH :
new_combination = self.apply_move_operator(new_combination)
if new_combination not in self.tabu_list:
self.tabu_list.add(new_combination)
solution = self.generate_solution(new_combination)
fitness = sum(b.fitness() for b in solution) / len(solution)
if fitness > self.fitness:
self.bins = solution
self.fitness = fitness
num_no_change = 0
combination = new_combination
current_iteration += 1
else :
current_iteration += 1
num_no_change += 1
return current_iteration, num_no_change, combination
def run3(self,chromosome):
"""
Runs the tabu search algorithm and returns the results at the end of the process.
:return: (num_iterations, num_no_changes, chosen_combination)
"""
combination = chromosome.pattern
self.bins =self.generate_solution(combination)
self.fitness = chromosome.fitness
self.tabu_list.add(combination)
current_iteration = 0
num_no_change = 0
while num_no_change < self.MAX_NO_CHANGE and current_iteration < self.MAX_ITERATIONS:
new_combination = self.apply_move_operator(combination)
while len(new_combination) > self.MAX_COMBINATION_LENGTH :
new_combination = self.apply_move_operator(new_combination)
if new_combination not in self.tabu_list:
self.tabu_list.add(new_combination)
solution = self.generate_solution(new_combination)
fitness = sum(b.fitness() for b in solution) / len(solution)
if fitness > self.fitness:
self.bins = solution
self.fitness = fitness
num_no_change = 0
combination = new_combination
current_iteration += 1
else :
current_iteration += 1
num_no_change += 1
return current_iteration, num_no_change, combination
def generate_solution(self, pattern):
"""
Generates a candidate solution based on the pattern given.
:param pattern: A pattern indicating the order in which heuristics need to be applied to get the solution.
:return: A list of bins to serve as a solution.
"""
solution = [Bin(self.bin_capacity)]
pattern_length = len(pattern)
for idx, item in enumerate(self.items):
h = pattern[idx % pattern_length]
solution = self.heuristic_map[h].apply(item, solution)
return solution
def apply_move_operator(self, pattern):
"""
Applies a random move operator to the given pattern.
:param pattern: The pattern to apply the move operator to.
:return: The pattern after the move operator has been applied.
"""
return random.choice(self.movers).apply(pattern, list(self.heuristic_map.keys()))
def RT(fileName):
boxContent=[]
# Ouvrir le fichier en mode lecture
with open(fileName, "r") as file:
Objets = []
for ligne in file:
taille = int(ligne.strip())
Objets.append(taille)
num_items = nb_objets
capacity = bin_size
items = Objets
items = [Item(size=int(i)) for i in items]
# Perform 1 independent iterations. (on peut la changer si veut exécuter successivement en ce script en changent 1 par le nombre d'expériances)
for iteration in range(1):
# print('Iteration numéro: '+ iteration)
# Randomize the order of the items in the item list.
shuffle(items)
thing = TabuSearch(capacity, items)
start_time = time.time()
total_iterations, stagnation, combination = thing.run()
end_time = time.time()
elapsed_time = end_time - start_time
# the result bins content
for singleBin in thing.bins:
newBoxContent=[]
for singleItem in singleBin.items:
newBoxContent.append(singleItem.size)
boxContent.append(newBoxContent)
return elapsed_time,len(thing.bins),boxContent
# Pour le test en ce fichier
# RT()