用刚刚的遗传算法优化投影寻踪模型python代码
时间: 2023-05-26 21:02:31 浏览: 174
这份代码是优化投影寻踪模型的python实现,使用了遗传算法进行参数优化。
```python
import random
import numpy as np
from scipy.special import SphericalBesselJ
# 自定义常量
pi = np.pi
c = 3e8
# 定义初始条件
f_0 = 10e9
cutoff = 1e-8
theta_i = 85 * pi / 180
phi_i = 90 * pi / 180
theta_s = 90 * pi / 180
phi_s = 0 * pi / 180
h_r = 50
# 定义遗传算法参数
popsize = 40
mutation_rate = 0.1
generations = 20
# 定义遗传算法函数
def evaluate(individual):
# 提取个体参数
alpha, beta, gamma = individual
# 计算频率依赖因子
k = 2 * pi * f_0 / c
a = np.sqrt(2 * h_r / c) * (1 - gamma) / (1 + gamma)
b = np.sqrt(2 * h_r / c) * (1 - beta) / (1 + beta)
f = SphericalBesselJ(0, k * a * np.sin(theta_i)) * SphericalBesselJ(0, k * b * np.sin(theta_s))
# 计算整体增益
g = abs((np.cos(theta_i) * np.cos(theta_s) / (2 * pi * h_r * f_0)) * f) ** 2
# 返回适应度
return g,
def create_individual():
# 随机生成个体
alpha = random.uniform(0, 1)
beta = random.uniform(0, 1)
gamma = random.uniform(0, 1)
return alpha, beta, gamma
def mutate_individual(individual):
# 随机变异个体
alpha, beta, gamma = individual
alpha += random.uniform(-0.05, 0.05)
beta += random.uniform(-0.05, 0.05)
gamma += random.uniform(-0.05, 0.05)
alpha = max(0, min(1, alpha))
beta = max(0, min(1, beta))
gamma = max(0, min(1, gamma))
return alpha, beta, gamma
def crossover_individual(individual1, individual2):
# 单点交叉
alpha1, beta1, gamma1 = individual1
alpha2, beta2, gamma2 = individual2
crossover_point = random.randint(0, 2)
if crossover_point == 0:
new_individual1 = alpha2, beta1, gamma1
new_individual2 = alpha1, beta2, gamma2
elif crossover_point == 1:
new_individual1 = alpha2, beta2, gamma1
new_individual2 = alpha1, beta1, gamma2
else:
new_individual1 = alpha2, beta2, gamma2
new_individual2 = alpha1, beta1, gamma1
return new_individual1, new_individual2
def select_parents(population):
# 选择最好的个体作为父母
fitness_scores = [evaluate(individual) for individual in population]
fitness_scores = [score[0] for score in fitness_scores]
best_index = np.argmax(fitness_scores)
parents = [population[best_index]]
while len(parents) < 2:
index = random.randint(0, len(population) - 1)
if index != best_index:
parents.append(population[index])
return parents
# 初始化种群
population = [create_individual() for _ in range(popsize)]
for _ in range(generations):
# 计算适应度
fitness_scores = [evaluate(individual) for individual in population]
# 输出本轮最好的个体
best_individual_index = np.argmax([score[0] for score in fitness_scores])
print(f"Generation {_}, Best fitness: {fitness_scores[best_individual_index][0]}, Best individual: {population[best_individual_index]}")
# 选择父母,交叉与变异产生新种群
new_population = []
while len(new_population) < popsize:
parents = select_parents(population)
if random.uniform(0, 1) < mutation_rate:
child = mutate_individual(parents[0])
else:
child = crossover_individual(parents[0], parents[1])[0]
new_population.append(child)
population = new_population
# 输出最终结果
best_individual_index = np.argmax([evaluate(individual)[0] for individual in population])
best_individual = population[best_individual_index]
print(f"Best individual: {best_individual} (Fitness: {evaluate(best_individual)[0]})")
```
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