def search(points, maxIterations, increase, decrease, maxCandidates):
current = {}
current["permutation"] = constructInitialSolution(points)
current["cost"] = tourCost(current["permutation"])
best = current
iteration = 0
while iteration < maxIterations:
# Memory based reaction
memoryBasedReaction(current, increase, decrease, iteration)
# Generate Candidate list from neighborhood from current
candidates = []
for index in range(0, maxCandidates):
candidates.append(generateCandidateNeighborhood(current))
# From candidate list get best
current, bestEdges = bestMove(candidates, iteration, len(points), best)
# Update tabu list with the current candidates features
for edge in bestEdges:
updateTabuList(edge, iteration)
# Compare cost of current with best and update best if less than current
if current["cost"] < best["cost"]:
best = current
iteration += 1
#return best
return best
def generateCandidateNeighborhood(current):
result = {}
permutation, edges = stochasticTwoOptWithEdges(current["permutation"])
candidate = {}
candidate["permutation"] = permutation
candidate["cost"] = tourCost(candidate["permutation"])
result["candidate"] = candidate
result["edges"] = edges
return result
def search(points, maxIterations, maxTabu, maxCandidates):
# 1. конструирование начального решения
# construct a random tour
best ={}
best["permutation"] = constructInitialSolution(points) # начальное решение
print len(points),len(best["permutation"])
print best["permutation"]
best["cost"] = tourCost(best["permutation"]) # ц.ф.
# 2. пустой табу-лист
tabuList =[]
# 3. главный цикл алгоритма на убывание
# StopCondition: maxIterations = 0, maxIterations = 100 в начале
# таким образом имеем 100 итераций - 100 локальных оптимумов
while maxIterations>0:
# 4. пустой список кандидатов
candidates = []
# Generate candidates using stocahstic 2-opt near current best candidate
# Use Tabu list to not revisit previous rewired edges
# 5. Ищем кандидатов, используя табуирование (даелаем мувы)
for index in range(0,maxCandidates):
# заполняем список решениями
candidates.append(generateCandidates(best, tabuList, points))
# Locate the best candidate
# sort the list of candidates by cost
# since it is an involved sort, we write a function for getting the least cost candidate
bestCandidate, bestCandidateEdges = locateBestCandidate(candidates)
# compare with current best and update if necessary
if bestCandidate["cost"] < best["cost"]:
# set current to the best, so thatwe can continue iteration
best = bestCandidate
# update tabu list
for edge in bestCandidateEdges:
if len(tabuList) < maxTabu:
tabuList.append(edge)
maxIterations -=1
return best
def generateCandidates(best, tabuList, points):
permutation, edges, result = None, None, {}
# собственно move
while permutation == None or isTabu(best["permutation"], tabuList):
permutation, edges = stochasticTwoOptWithEdges(best["permutation"])
candidate ={}
candidate["permutation"] = permutation
candidate["cost"] = tourCost(candidate["permutation"])
result["candidate"] = candidate
result["edges"] = edges
return result # возвращает решение
