def bestOf(neighbors, p): ###
best = neighbors.pop()
bestValue = TSP.evaluate(best, p)
for neighbor in neighbors:
nValue = TSP.evaluate(neighbor, p)
if nValue < bestValue:
best = neighbor
bestValue = nValue
return best, bestValue
def firstChoice(p):
current = TSP.randomInit(p) # 'current' is a list of city ids
valueC = TSP.evaluate(current, p)
i = 0
while i < LIMIT_STUCK:
successor = randomMutant(current, p)
valueS = TSP.evaluate(successor, p)
if valueS < valueC:
current = successor
valueC = valueS
i = 0 # Reset stuck counter
else:
i += 1
return current, valueC
def steepestAscent(p):
current = TSP.randomInit(p) # 'current' is a list of city ids
valueC = TSP.evaluate(current, p)
while True:
neighbors = mutants(current, p)
(successor, valueS) = bestOf(neighbors, p)
if valueS >= valueC:
break
else:
current = successor
valueC = valueS
return current, valueC
