def runTSP_5(graph, popSize=100, gens=200):
print('initialise population...')
population = [
randomRouteGenerator(list(range(len(graph))))
for each in list(range(popSize))
]
firstGenPopulation = population
print('firstGenPopulation: ', firstGenPopulation)
counter = 0
while counter < gens:
print('evolution start...')
parents = parentSelection(graph, population)
# print('parents: ', parents)
offsprings = recombination(parents, len(parents) * 3)
# print('offsprings: ', offsprings)
for offspring in offsprings:
mutatedOffspring = mutation(offspring)
# print('mutatedOffspring: ', mutatedOffspring)
population.append(mutatedOffspring)
population = survivorSelection(population, graph, popSize)
populationBestRoute = reduce(
lambda candidateA, candidateB: candidateB
if getCostOfRoute(candidateA, graph) > getCostOfRoute(
candidateB, graph) else candidateA, population)
print(f'population {counter} best cost route: {populationBestRoute}',
getCostOfRoute(populationBestRoute, graph))
counter += 1
def runTSP_2(graph):
cityList = list(range(len(graph)))
route = intilializer(cityList)
# print('route: ', route)
startTime = time.time()
currentTime = time.time()
TERMINATION_TIME = 10
bestCost = getCostOfRoute(route, graph)
bestRoute = []
while (currentTime - startTime) < TERMINATION_TIME:
neighbourhood = generateNeighbourhood(route)
previousCost = bestCost
for neighbour in neighbourhood:
neigbourCost = getCostOfRoute(neighbour, graph)
# print('neigbour: ', neighbour, ' cost', neigbourCost)
if neigbourCost < bestCost:
bestCost = neigbourCost
bestRoute = neighbour
route = neighbour
# print('route: ', route , ' cost: ', bestCost )
# print('bestCost: ', bestCost , ' previousCost: ', previousCost )
if bestCost == previousCost:
route = intilializer(cityList)
currentTime = time.time()
def bestNeigbourhoodMember(neighbourhood, graph):
bestRoute = neighbourhood[0]
bestCost = getCostOfRoute(bestRoute, graph)
for i, neighbour in enumerate(neighbourhood):
neigbourCost = getCostOfRoute(neighbour, graph)
if neigbourCost < bestCost:
bestRoute = neighbour
bestCost = neigbourCost
return bestRoute, bestCost
def runTSP_2(graph, terminationTime):
cityList = list(range(len(graph)))
route = randomRouteGenerator(cityList)
startTime = time.time()
currentTime = time.time()
bestCost = getCostOfRoute(route, graph)
bestRoute = route
print('start cost: ', bestCost)
while (currentTime - startTime) < terminationTime:
neighbourhood = generateNeighbourhood(route)
bestNeighbour, bestNeighbourCost = bestNeigbourhoodMember(
neighbourhood, graph)
if bestNeighbour == route:
if bestNeighbourCost < bestCost:
print('new best cost: ', bestCost)
bestCost = bestNeighbourCost
bestRoute = bestNeighbour
route = randomRouteGenerator(cityList)
else:
route = bestNeighbour
currentTime = time.time()
print('bestRoute: ', bestRoute, ' bestCost: ', bestCost)
def parentSelection(graph, population, numOfparents=20, k=5):
parents = []
while len(parents) <= numOfparents:
tournamentSample = []
counterB = 0
nextParentIndex = 0
pastParents = len(parents)
while counterB < k:
candidate = choice(population)
if candidate not in tournamentSample:
tournamentSample.append(candidate)
counterB += 1
possibleParents = sorted(
tournamentSample,
key=lambda candidate: getCostOfRoute(candidate, graph),
reverse=False)
pastParents = len(parents)
for parent in possibleParents:
if parent not in parents:
parents.append(parent)
break
if pastParents == len(parents):
parents.append(possibleParents[0])
# print('parents: ', parents)
return parents
def runTSP_1(routes, cityList) :
bestRoute = []
bestCost = 100
for route in routes :
routeCost = getCostOfRoute(route, cityList)
if (routeCost < bestCost) :
bestCost = routeCost
bestRoute = route
print('best route - ', bestRoute, ' cost: ', bestCost)
def survivorSelection(population, graph, genSizeLimit):
populationSize = len(population)
if populationSize >= genSizeLimit:
sortedPopulation = sorted(
population, key=lambda candidate: getCostOfRoute(candidate, graph))
newPopulation = sortedPopulation[0:genSizeLimit - 1]
# print('newPopulation: ', newPopulation)
return newPopulation
def parentSelection(graph, population, numOfparents=5, k=20):
parents = []
counterA = 0
while counterA < numOfparents:
tournamentSample = []
counterB = 0
while counterB < k:
candidate = population[randint(0, len(population) - 1)]
if candidate not in tournamentSample:
tournamentSample.append(candidate)
counterB += 1
parent = reduce(
lambda candidateA, candidateB: candidateB
if getCostOfRoute(candidateA, graph) > getCostOfRoute(
candidateB, graph) else candidateA, tournamentSample)
if parent not in parents:
parents.append(parent)
counterA += 1
# print('parents: ', parents)
return parents
def randomSearch(graph, terminationTime) :
bestRoute = []
bestCost = math.inf
cityList = list(range(len(graph)))
i = 0
print('begin random search...')
startTime = time.time()
while time.time() - startTime < terminationTime :
route = randomRouteGenerator(cityList)
routeCost = getCostOfRoute(route, graph)
if routeCost < bestCost :
bestCost = routeCost
bestRoute = route
i+=1
print('Number of routes checked - ', i, ' in approx.', terminationTime, ' seconds')
print('best route - ', bestRoute, ' cost: ', bestCost)
def naiveSearch(graph, terminationTime) :
bestRoute = []
bestCost = math.inf
routes = permuteRoutes(graph)
print('begin naive search...')
startTime = time.time()
for index, route in enumerate(routes) :
if time.time() - startTime > terminationTime :
numOfRoutesChecked = index
break
routeCost = getCostOfRoute(route, graph)
if routeCost < bestCost :
bestCost = routeCost
bestRoute = route
print('Number of routes checked - ', numOfRoutesChecked, ' in approx.', terminationTime, ' seconds')
print('best route - ', bestRoute, ' cost: ', bestCost)