可以帮我找NSGA-II的代码吗
时间: 2024-04-30 16:18:58 浏览: 24
当然可以,NSGA-II是一个经典的多目标优化算法,有很多代码实现。以下是一个 Python 实现的例子,你可以参考一下:
```python
import random
import copy
import numpy as np
class NSGAII():
def __init__(self, obj_func, n_individual=100, n_generation=50, x_min=0, x_max=1, n_obj=2, n_var=2, p_crossover=0.9, p_mutation=0.1):
self.obj_func = obj_func
self.n_individual = n_individual
self.n_generation = n_generation
self.x_min = x_min
self.x_max = x_max
self.n_obj = n_obj
self.n_var = n_var
self.p_crossover = p_crossover
self.p_mutation = p_mutation
# 初始化种群
def initialize_population(self):
population = []
for i in range(self.n_individual):
individual = {}
individual['x'] = np.random.uniform(self.x_min, self.x_max, self.n_var).tolist()
individual['fitness'] = [0.0] * self.n_obj
individual['rank'] = None
individual['crowding_distance'] = None
population.append(individual)
return population
# 计算适应度
def evaluate_fitness(self, population):
for individual in population:
x = individual['x']
fitness = self.obj_func(x)
individual['fitness'] = fitness
# 计算拥挤度
def calculate_crowding_distance(self, front):
n_individual = len(front)
for individual in front:
individual['crowding_distance'] = 0.0
for i in range(self.n_obj):
sorted_front = sorted(front, key=lambda x: x['fitness'][i])
sorted_front[0]['crowding_distance'] = sorted_front[-1]['crowding_distance'] = 1.0
for j in range(1, n_individual-1):
sorted_front[j]['crowding_distance'] += (sorted_front[j+1]['fitness'][i] - sorted_front[j-1]['fitness'][i]) / (sorted_front[-1]['fitness'][i] - sorted_front[0]['fitness'][i])
# 快速非支配排序
def fast_non_dominated_sort(self, population):
S = [[] for i in range(self.n_individual)]
front = [[]]
n_individual = len(population)
n_domination = [0] * n_individual
for i in range(n_individual):
for j in range(i+1, n_individual):
if population[i]['fitness'][0] < population[j]['fitness'][0] and population[i]['fitness'][1] < population[j]['fitness'][1]:
S[i].append(j)
n_domination[j] += 1
elif population[j]['fitness'][0] < population[i]['fitness'][0] and population[j]['fitness'][1] < population[i]['fitness'][1]:
S[j].append(i)
n_domination[i] += 1
if n_domination[i] == 0:
population[i]['rank'] = 0
front[0].append(population[i])
i = 0
while len(front[i]) > 0:
Q = []
for individual in front[i]:
for j in S[individual]:
n_domination[j] -= 1
if n_domination[j] == 0:
population[j]['rank'] = i+1
Q.append(population[j])
i += 1
front.append(Q)
return front[:-1]
# 交叉操作
def crossover(self, parent1, parent2):
child1, child2 = copy.deepcopy(parent1), copy.deepcopy(parent2)
if random.random() < self.p_crossover:
alpha = np.random.uniform(0, 1, self.n_var)
child1['x'] = (alpha * np.array(parent1['x']) + (1-alpha) * np.array(parent2['x'])).tolist()
child2['x'] = ((1-alpha) * np.array(parent1['x']) + alpha * np.array(parent2['x'])).tolist()
return child1, child2
# 变异操作
def mutation(self, individual):
if random.random() < self.p_mutation:
index = random.choice(range(self.n_var))
individual['x'][index] = np.random.uniform(self.x_min, self.x_max)
return individual
# 选择操作
def selection(self, population):
front = self.fast_non_dominated_sort(population)
selected = []
n_selected = 0
i = 0
while n_selected + len(front[i]) <= self.n_individual:
self.calculate_crowding_distance(front[i])
selected += front[i]
n_selected += len(front[i])
i += 1
if n_selected < self.n_individual:
self.calculate_crowding_distance(front[i])
sorted_front = sorted(front[i], key=lambda x: -x['crowding_distance'])
selected += sorted_front[:self.n_individual-n_selected]
return selected
# NSGA-II算法主程序
def run(self):
population = self.initialize_population()
for i in range(self.n_generation):
self.evaluate_fitness(population)
offspring = []
while len(offspring) < self.n_individual:
parent1, parent2 = random.sample(population, 2)
child1, child2 = self.crossover(parent1, parent2)
child1 = self.mutation(child1)
child2 = self.mutation(child2)
offspring.append(child1)
offspring.append(child2)
population += offspring
population = self.selection(population)
self.evaluate_fitness(population)
front = self.fast_non_dominated_sort(population)
pareto_front = []
for individual in front[0]:
pareto_front.append(individual['fitness'])
return pareto_front
```
其中,`obj_func` 是用户自定义的目标函数,输入为决策变量向量,输出为目标函数向量。`n_individual` 是种群大小,`n_generation` 是迭代次数,`n_obj` 是目标函数个数,`n_var` 是决策变量个数,`p_crossover` 和 `p_mutation` 分别是交叉概率和变异概率。在 `run()` 函数中,返回的是 Pareto 前沿集合的目标函数值列表。
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