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| import numpy as np
import matplotlib.pyplot as plt
import random
from deap import creator, tools, base
creator.create('FitnessMin', base.Fitness, weights=(-1.0, 1.0))
creator.create('Individual', list, fitness=creator.FitnessMin)
gen_size = 54
toolbox = base.Toolbox()
toolbox.register('Sequence', np.random.permutation, gen_size)
toolbox.register('Individual', tools.initIterate,
creator.Individual, toolbox.Sequence)
toolbox.register('Population', tools.initRepeat, list, toolbox.Individual)
nodeDict = {}
with open("node.txt", 'r') as f:
tmp = f.readlines()
for t in tmp:
t = t.split()
nodeDict[t[0]] = list(map(int, t[1:]))
nodeLine = {}
with open("line.txt", 'r') as f:
tmp = f.readlines()
for t in tmp:
t = t.split()
nodeLine[t[0]] = list(map(int, t[1:]))
edgeTime = {}
with open("time.txt", 'r') as f:
tmp = f.readlines()
for t in tmp:
t = t.split()
edgeTime[t[0]+t[1]] = float(t[2])
edgeTime[t[1]+t[0]] = float(t[2])
edgeCost = {}
with open("dis.txt", 'r') as f:
tmp = f.readlines()
for t in tmp:
t = t.split()
edgeCost[t[0]+t[1]] = float(t[2])
edgeCost[t[1]+t[0]] = float(t[2])
T = 5
def dfs(ind, source, target):
path = []
visited = []
stack = [source]
lens = []
while stack:
cur = stack[-1]
if cur not in visited:
path.append(cur)
visited.append(cur)
allow = np.array(nodeDict[str(cur)])
priority_ls = np.asarray(ind)[np.asarray(allow)-1]
index = np.argsort(priority_ls)
stack.extend(allow[index])
lens.append(len(allow[index]))
if target in stack:
path.append(target)
break
else:
stack.pop()
lens[-1] -= 1
if lens[-1] == 0:
lens.pop()
path.pop()
return path
def eval(ind):
path = dfs(ind, source, target)
time = TIME(path)
cost = COST(path)
return (time), (cost)
def TIME(path):
time = edgeTime[str(path[0])+str(path[1])]
if len(path) > 2:
pre = set(nodeLine[str(path[0])]) & set(
nodeLine[str(path[1])]) & set(nodeLine[str(path[2])])
time += edgeTime[str(path[1])+str(path[2])]
for i in range(2, len(path)-1):
time += edgeTime[str(path[i])+str(path[i+1])]
now = set(nodeLine[str(path[i])]) & set(nodeLine[str(path[i+1])])
if not pre & now:
pre = now
time += T
return time
def COST(path):
dis = 0
for i in range(len(path)-1):
dis += edgeCost[str(path[i])+str(path[i+1])]
price = [2, 3, 4, 5, 6, 7]
rank = [6, 11, 17, 24, 32]
rank.append(dis)
rank.sort()
cost = price[rank.index(dis)]
return cost
for source in [54]:
for target in range(53, 54):
if target == source:
continue
toolbox.register('evaluate', eval)
toolbox.register('select', tools.selTournament, tournsize=2)
toolbox.register('mate', tools.cxOrdered)
toolbox.register('mutate', tools.mutShuffleIndexes, indpb=0.5)
stats = tools.Statistics(key=lambda ind: ind.fitness.values)
stats.register('avg', np.mean)
stats.register('std', np.std)
stats.register('min', np.min)
stats.register('max', np.max)
logbook = tools.Logbook()
logbook.header = 'gen', 'avg', 'std', 'min', 'max'
pop_size = 100
N_GEN = 20
CXPB = 0.8
MUTPB = 0.2
pop = toolbox.Population(n=pop_size)
invalid_ind = [ind for ind in pop if not ind.fitness.valid]
fitnesses = list(map(toolbox.evaluate, invalid_ind))
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
record = stats.compile(pop)
logbook.record(gen=0, **record)
for gen in range(1+N_GEN):
selectTour = toolbox.select(pop, pop_size)
selectInd = list(map(toolbox.clone, selectTour))
for child1, child2 in zip(selectInd[::2], selectInd[1::2]):
if random.random() < CXPB:
toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
for ind in selectInd:
if random.random() < MUTPB:
toolbox.mutate(ind)
del ind.fitness.values
invalid_ind = [ind for ind in selectInd if not ind.fitness.valid]
fitnesses = list(map(toolbox.evaluate, invalid_ind))
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
combinedPop = pop + selectInd
pop = tools.selBest(combinedPop, pop_size)
record = stats.compile(pop)
logbook.record(gen=gen, **record)
print(logbook)
bestInd = tools.selBest(pop, 1)[0]
bestFit = bestInd.fitness.values
print(f'{source} to {target}')
print('最短耗时为:', bestFit[0])
print('对应费用为:', bestFit[1])
print('对应路径为:', dfs(bestInd, source, target))
min = logbook.select('min')
avg = logbook.select('avg')
gen = logbook.select('gen')
plt.plot(gen, min, 'b-', label='MIN_FITNESS')
plt.plot(gen, avg, 'r-', label='AVG_FITNESS')
plt.xlabel('gen')
plt.ylabel('fitness')
plt.legend(loc='best')
plt.tight_layout()
plt.show()
|
