def neighbours_LNS(data, perm, size = 2):
# Returns the Large Neighbourhood Search neighbours
candidates = [perm]
# Bound the number of neighbourhoods in case there are too many jobs
neighbourhoods = list(combinations(range(len(perm)), size))
random.shuffle(neighbourhoods)
for subset in neighbourhoods[:flow.MAX_LNS_NEIGHBOURHOODS]:
# Keep track of the best candidate for each neighbourhood
best_make = flow.makespan(data, perm)
best_perm = perm
# Enumerate every permutation of the selected neighbourhood
for ordering in permutations(subset):
candidate = perm[:]
for i in range(len(ordering)):
candidate[subset[i]] = perm[ordering[i]]
res = flow.makespan(data, candidate)
if res < best_make:
best_make = res
best_perm = candidate
# Record the best candidate as part of the larger neighbourhood
candidates.append(best_perm)
return candidates
def neighbours_LNS(data, perm, size=2):
# Returns the Large Neighbourhood Search neighbours
candidates = [perm]
# Bound the number of neighbourhoods in case there are too many jobs
neighbourhoods = list(combinations(range(len(perm)), size))
random.shuffle(neighbourhoods)
for subset in neighbourhoods[:flow.MAX_LNS_NEIGHBOURHOODS]:
# Keep track of the best candidate for each neighbourhood
best_make = flow.makespan(data, perm)
best_perm = perm
# Enumerate every permutation of the selected neighbourhood
for ordering in permutations(subset):
candidate = perm[:]
for i in range(len(ordering)):
candidate[subset[i]] = perm[ordering[i]]
res = flow.makespan(data, candidate)
if res < best_make:
best_make = res
best_perm = candidate
# Record the best candidate as part of the larger neighbourhood
candidates.append(best_perm)
return candidates
def neighbours_LNS(data, perm, size = 2):
# Returns the Large Neighbourhood Search neighbours
# 返回最大的相邻搜索点集
candidates = [perm]
# 限定最大的搜索数量
neighbourhoods = list(combinations(range(len(perm)), size))
random.shuffle(neighbourhoods)
for subset in neighbourhoods[:flow.MAX_LNS_NEIGHBOURHOODS]:
# 记录每个候选集中表现最好的候选点
best_make = flow.makespan(data, perm)
best_perm = perm
# Enumerate every permutation of the selected neighbourhood
# 枚举选中邻域的每个排列
for ordering in permutations(subset):
candidate = perm[:]
for i in range(len(ordering)):
candidate[subset[i]] = perm[ordering[i]]
res = flow.makespan(data, candidate)
if res < best_make:
best_make = res
best_perm = candidate
# 将最好的结果保存到候选集尾部
candidates.append(best_perm)
return candidates
def heur_random_hillclimbing(data, candidates):
# Returns a candidate with probability proportional to its rank in sorted quality
scores = [(flow.makespan(data, perm), perm) for perm in candidates]
i = 0
while (random.random() < 0.5) and (i < len(scores) - 1):
i += 1
return sorted(scores)[i][1]
def heur_random_hillclimbing(data, candidates):
# 考虑每个方案的排名，进行加权随机选取
scores = [(flow.makespan(data, perm), perm) for perm in candidates]
i = 0
while (random.random() < 0.5) and (i < len(scores) - 1):
i += 1
return sorted(scores)[i][1]
def heur_random_hillclimbing(data, candidates):
# Returns a candidate with probability proportional to its rank in sorted quality
scores = [(flow.makespan(data, perm), perm) for perm in candidates]
i = 0
while (random.random() < 0.5) and (i < len(scores) - 1):
i += 1
return sorted(scores)[i][1]
def heur_hillclimbing(data, candidates):
# Returns the best candidate in the list
scores = [(flow.makespan(data, perm), perm) for perm in candidates]
return sorted(scores)[0][1]
def heur_hillclimbing(data, candidates):
# 返回候选集中最好的方案
scores = [(flow.makespan(data, perm), perm) for perm in candidates]
return sorted(scores)[0][1]
def heur_hillclimbing(data, candidates):
# Returns the best candidate in the list
scores = [(flow.makespan(data, perm), perm) for perm in candidates]
return sorted(scores)[0][1]
