Python_多目标遗传算法
时间: 2023-11-30 17:04:40 浏览: 42
多目标遗传算法是一种优化算法,用于解决多个目标函数的优化问题。在很多实际问题中,我们需要同时优化多个目标函数,但是这些目标函数通常是互相矛盾的,即优化其中一个目标函数会导致其他目标函数的值变差。多目标遗传算法通过维护一组解的集合,称为“种群”,并通过遗传操作(如交叉和变异)来不断改进这个种群,从而逐步逼近最优解的集合,称为“帕累托前沿”。
以下是一个简单的Python多目标遗传算法的示例代码:
```python
import random
# 定义目标函数
def obj_func(x):
return [x[0]**2, (x[0]-2)**2]
# 定义种群大小和变量范围
pop_size = 50
var_range = [-5, 5]
# 初始化种群
pop = [[random.uniform(var_range[0], var_range[1]) for _ in range(1)] for _ in range(pop_size)]
# 迭代次数
num_iter = 100
# 开始迭代
for i in range(num_iter):
# 计算每个个体的目标函数值
obj_vals = [obj_func(x) for x in pop]
# 计算每个个体的适应度值
fitness_vals = []
for j in range(pop_size):
fitness_vals.append(sum([1 for k in range(pop_size) if obj_vals[k][0] <= obj_vals[j][0] and obj_vals[k][1] <= obj_vals[j][1]]))
# 选择操作
new_pop = []
for j in range(pop_size):
parent1 = random.choices(pop, weights=fitness_vals)[0]
parent2 = random.choices(pop, weights=fitness_vals)[0]
child = [(parent1[0] + parent2[0])/2]
new_pop.append(child)
# 变异操作
for j in range(pop_size):
if random.random() < 0.1:
new_pop[j][0] += random.uniform(-0.5, 0.5)
# 更新种群
pop = new_pop
# 输出帕累托前沿
obj_vals = [obj_func(x) for x in pop]
pareto_front = []
for i in range(pop_size):
if sum([1 for j in range(pop_size) if obj_vals[j][0] <= obj_vals[i][0] and obj_vals[j][1] <= obj_vals[i][1]]) == 1:
pareto_front.append(pop[i])
print(pareto_front)
```