def inversion_mutation(individual, mutation_rate):
result = Individual(individual.dna, individual.mutation_operator, individual.crossover_operator)
for swap1 in range(individual.__len__()):
if random.random() < mutation_rate:
swap2 = int(random.random() * individual.__len__())
a_list = result.dna[min(swap1, swap2):max(swap1, swap2) + 1]
a_list = a_list[::-1]
for i in range(min(swap1, swap2), max(swap1, swap2) + 1):
result.dna[i] = a_list[i - min(swap1, swap2)]
return result
def swap_mutation(individual, mutation_rate):
result = Individual(individual.dna, individual.mutation_operator, individual.crossover_operator)
for swap1 in range(individual.__len__()):
if random.random() < mutation_rate:
swap2 = int(random.random() * individual.__len__())
city1 = result.dna[swap1]
city2 = result.dna[swap2]
result.dna[swap1] = city2
result.dna[swap2] = city1
return result
def scramble_mutation(individual, mutation_rate):
result = Individual(individual.dna, individual.mutation_operator, individual.crossover_operator)
for swap1 in range(individual.__len__()):
if random.random() < mutation_rate:
swap2 = int(random.random() * individual.__len__())
a_list = []
for i in range(min(swap1, swap2), max(swap1, swap2) + 1):
a_list.append(i)
random.shuffle(a_list)
cop = list(result.dna)
for i in range(min(swap1, swap2), max(swap1, swap2) + 1):
result.dna[i] = cop[a_list[i - min(swap1, swap2)]]
return result
def cycle_crossover(parent1, parent2):
child = []
p1 = []
p2 = []
# 从第一个个体中随机选择一段城市，拼接第二个个体中所有不重复的城市，生成的新的个体
geneA = int(random.random() * len(parent1.dna))
dic = {}
dic1 = {}
for i in range(len(parent1.dna)):
dic1[parent1.dna[i]] = parent2.dna[i]
dic[parent1.dna[geneA]] = parent2.dna[geneA]
k = parent2.dna[geneA]
while k != parent1.dna[geneA]:
if k is dic1[k]:
break
dic[k] = dic1[k]
k = dic1[k]
for i in range(len(parent1.dna)):
if parent1.dna[i] in dic.keys():
child.append(parent1.dna[i])
else:
child.append(parent2.dna[i])
return Individual(child, parent1.mutation_operator,
parent1.crossover_operator)
def initial_population(self, pop_size):
population = Population(self.alg.selection_strategy)
for i in range(0, pop_size):
population.append(
Individual(random.sample(self.city_list, len(self.city_list)),
self.alg.mutation_operator,
self.alg.crossover_operator))
return population
def insert_mutation(individual, mutation_rate):
result = Individual(individual.dna, individual.mutation_operator, individual.crossover_operator)
for insert1 in range(individual.__len__()):
if random.random() < mutation_rate:
insert2 = int(random.random() * individual.__len__())
if insert1 == insert2:
return result
city1 = result.dna[insert1]
city2 = result.dna[insert2]
insert3 = min(insert1, insert2)
insert4 = max(insert1, insert2)
for i in range(insert3 + 1, insert4):
result.dna[i + 1] = result.dna[i]
if insert1 > insert2:
result.dna[insert2 + 1] = city1
else:
result.dna[insert1 + 1] = city2
return result
def order_crossover(parent1, parent2):
child = []
p1 = []
p2 = []
# 从第一个个体中随机选择一段城市，拼接第二个个体中所有不重复的城市，生成的新的个体
geneA = int(random.random() * len(parent1.dna))
geneB = int(random.random() * len(parent2.dna))
start = min(geneA, geneB)
end = max(geneA, geneB)
for i in range(start, end + 1):
p1.append(parent1.dna[i])
for i in range(end + 1, end + len(parent1.dna) + 1):
cou = i
if cou >= len(parent1.dna):
cou -= len(parent1.dna)
if parent2.dna[cou] not in p1:
p2.append(parent2.dna[cou])
child = p2[len(parent2.dna) - end:] + p1 + p2[:len(parent2.dna) - end]
return Individual(child, parent1.mutation_operator,
parent1.crossover_operator)
def PBX_crossover(parent1, parent2):
child = []
# 从第一个个体中随机选择一段城市，拼接第二个个体中所有不重复的城市，生成的新的个体
geneA = int(random.random() * len(parent1.dna))
geneB = int(random.random() * len(parent2.dna))
start = min(geneA, geneB)
end = max(geneA, geneB)
dic = {}
child = parent1.dna
q1 = []
q2 = []
nums = []
for i in range(start, end + 1):
dic[parent2.dna[i]] = parent1.dna[i]
q1.append(parent1.dna[i])
q2.append(parent2.dna[i])
nums.append(i)
for i in range(len(q2)):
if q2[i] not in q1:
k = q2[i]
dict = {}
while k in q2:
if k is dic[k] or k in dict.keys():
break
dict[k] = dic[k]
k = dic[k]
flag = True
if k in dic.keys() and k is dic[k]:
flag = False
if k in parent2.dna and flag is True:
t = parent2.dna.index(k)
child[t] = q2[i]
nums.append(i)
for i in range(len(child)):
if i not in nums:
child[i] = parent2.dna[i]
return Individual(child, parent1.mutation_operator,
parent1.crossover_operator)