from TravellingSalesmanProblem import getCostOfRoute, readCSV
'''
Lab instructions:
https://vle.aston.ac.uk/bbcswebdav/pid-1674349-dt-content-rid-10821790_1/xid-10821790_1
'''
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 permuteRoutes(cityList) :
return list(itertools.permutations(range(len(cityList))))
runTSP_1(permuteRoutes(cityList), readCSV())
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
# firstGenPopulationCostAverage = reduce(lambda accum, current: accum + current, firstGenPopulation) / popSize
# LastGenPopulationCostAverage = reduce(lambda accum, current: accum + current, population) / popSize
# print(f'gen num {gens}: {population}')
# print(f'first generation average cost: {firstGenPopulationCostAverage}')
# print(f'last generation average cost: {LastGenPopulationCostAverage}')
print('start TSP5...')
runTSP_5(readCSV())
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()
# print('time left', TERMINATION_TIME - (currentTime - startTime))
# print('bestRoute: ', bestRoute , ' bestCost: ', bestCost )
runTSP_2(readCSV())
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)
def runTSP_1(graph, terminationTime) :
randomSearch(graph, terminationTime)
# naiveSearch(graph, terminationTime)
def permuteRoutes(cityList) :
print('cityList length: ', len(cityList))
return permutations(range(len(cityList)))
runTSP_1(readCSV(), 3)