def heuristique_ils2(tasks):
ind = np.arange(len(tasks))
np.random.shuffle(ind)
best_score = eval_sol.eval_solution(tasks[ind])
print("init pos: " + str(ind))
print("init score: " + str(best_score))
# take 5 nbh per grp
cpt = 0
nb_nbh_grps = int(len(ind) / 8)
splits = np.split(ind, nb_nbh_grps)
for i in range(len(splits)):
remain_splits = splits.copy()
explore = remain_splits.pop(i)
for perm in multiset_permutations(explore):
tmp_ind = np.array([], dtype=int)
for k in range(len(splits)):
if k == i:
tmp_ind = np.append(tmp_ind, perm)
else:
tmp_ind = np.append(tmp_ind, splits[k])
score = eval_sol.eval_solution(tasks[tmp_ind])
if score < best_score:
best_score = score
print("new best score found: " + str(best_score) +
", in split_nb: " + str(i))
print("tmp_ind: " + str(tmp_ind))
cpt += 1
print()
print(cpt)
return
def heuristique_vnd(tasks):
ind = np.arange(len(tasks))
np.random.shuffle(ind)
best_score = eval_sol.eval_solution(tasks[ind])
print("init pos: " + str(ind))
print("init score: " + str(best_score))
fixed_tasks = np.array([], dtype=int)
for t_i in range(len(tasks)):
found = False
fixed_tasks = np.append(fixed_tasks, t_i) # rotate over t_i
for tmp_ind in rot_fix(ind, fixed_tasks):
print("fixed_tasks: " + str(fixed_tasks))
print("tmp_ind: " + str(tmp_ind))
score = eval_sol.eval_solution(tasks[tmp_ind])
if score < best_score:
best_score = score
print("new best score found: " + str(best_score) +
", fixed_tasks: " + str(fixed_tasks))
print("tmp_ind: " + str(tmp_ind))
found = True
if not found:
fixed_tasks = fixed_tasks[:-1] # delete last added one
return
def heuristique_hill_climbing(tasks):
ind = sort_by_importance(tasks)
best_score = eval_sol.eval_solution(tasks[ind])
print("init pos: " + str(ind))
print("init score: " + str(best_score))
for i in range(2, len(tasks)):
print("i: " + str(i) + ", perm: " + str(ind[:i]))
for perm in multiset_permutations(ind[:i]):
new_ind = np.append(perm, ind[i:].reshape(-1, 1))
score = eval_sol.eval_solution(tasks[new_ind])
if score < best_score:
best_score = score
print("new best score found: " + str(best_score))
print(new_ind)
return
def local_search(ind, tasks, best_score):
# taking first 8 neighbors
for perm in multiset_permutations(ind[:8]):
new_ind = np.append(perm, ind[8:].reshape(-1, 1))
score = eval_sol.eval_solution(tasks[new_ind])
if score < best_score:
best_score = score
print("new best score found: " + str(best_score))
print(new_ind)
return best_score, new_ind
def heuristique_local_search(tasks):
ind = sort_by_importance(tasks)
init_score = eval_sol.eval_solution(tasks[ind])
print("init pos: " + str(ind))
print("init score: " + str(init_score))
best_score, new_ind = local_search(ind, tasks, init_score)
if best_score == init_score:
ind = new_ind
return best_score, ind
def heuristique_vnd_2(tasks):
ind = np.arange(len(tasks))
np.random.shuffle(ind)
best_score = eval_sol.eval_solution(tasks[ind])
print("init pos: " + str(ind))
print("init score: " + str(best_score))
cpt = 0
for t_i in range(len(tasks)):
subset = ind[t_i:t_i + 5]
for perm in multiset_permutations(subset):
tmp_ind = np.concatenate((ind[0:t_i], perm, ind[t_i + 6:]))
score = eval_sol.eval_solution(tasks[tmp_ind])
if score < best_score:
best_score = score
print("new best score found: " + str(best_score))
print("tmp_ind: " + str(tmp_ind))
cpt += 1
print(cpt)
return
def brute_force(tasks, last_ind=None):
best_score = np.inf
i = -1
first = True
for ind in multiset_permutations(np.arange(len(tasks))):
i += 1
if last_ind is not None and i < last_ind:
continue
else:
if first:
first = False
print("Let's explore !")
score = eval_sol.eval_solution(tasks[ind])
best_score = min(best_score, score)
if best_score == score:
print("found new best score: " + str(best_score) + ", ind: " +
str(i))
return best_score