def search(points, maxIterations, maxTabu, maxCandidates):
# construct a random tour
best = {}
best["loopIter"] = constructInitialSolution(points)
"""Call constraint set solver to get distance d between 2 polyhedra"""
best["cost"] = tourCost(best["loopIter"])
tabuList = []
while maxIterations > 0:
# Generate candidates using stocahstic 2-opt near current best candidate
# Use Tabu list to not revisit previous rewired edges
candidates = []
for index in range(0, maxCandidates):
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, {}
while permutation == None or isTabu(best["loopIter"], tabuList):
permutation, edges = stochasticTwoOptWithEdges(best["loopIter"])
candidate = {}
candidate["loopIter"] = permutation
"""Call constraint set solver to get distance d between 2 polyhedra"""
candidate["cost"] = tourCost(candidate["loopIter"])
result["candidate"] = candidate
result["edges"] = edges
return result
