用刚刚的遗传算法优化投影寻踪模型python代码

好的，以下是使用遗传算法优化投影寻踪模型的Python代码示例：

import numpy as np
from numba import njit
from functools import lru_cache
from scipy.optimize import minimize
import random

# 定义投影寻踪模型
@lru_cache(maxsize=None)
@njit
def trace_model(x, y, theta, l):
    if theta == 0:
        theta = 0.00001
    t = np.abs(l / np.cos(theta))
    x2 = x + t * np.sin(theta)
    y2 = y + t * np.cos(theta)
    return x2, y2

# 定义适应度函数
def fitness_function(x, y, theta, l, target_x, target_y):
    x2, y2 = trace_model(x, y, theta, l)
    distance = np.sqrt((x2 - target_x) ** 2 + (y2 - target_y) ** 2)
    return 1 / distance

# 定义遗传算法
def genetic_algorithm(population_size, mutation_rate, generations, x, y, theta, l, target_x, target_y):
    # 初始化种群
    population = []
    for i in range(population_size):
        chromosome = []
        for j in range(4):
            if j == 0:
                gene = random.uniform(x - 10, x + 10)
            elif j == 1:
                gene = random.uniform(y - 10, y + 10)
            elif j == 2:
                gene = random.uniform(theta - np.pi / 4, theta + np.pi / 4)
            else:
                gene = random.uniform(l - 10, l + 10)
            chromosome.append(gene)
        population.append(chromosome)

    # 迭代进化
    for generation in range(generations):
        # 计算适应度
        fitness = []
        for chromosome in population:
            x, y, theta, l = chromosome
            fitness.append(fitness_function(x, y, theta, l, target_x, target_y))

        # 选择
        population_fitness = list(zip(population, fitness))
        population_fitness.sort(key=lambda x: x[1], reverse=True)
        population = [x[0] for x in population_fitness[:int(population_size / 2)]]

        # 交叉
        for i in range(int(population_size / 2)):
            parent1 = random.choice(population)
            parent2 = random.choice(population)
            child1 = parent1[:2] + parent2[2:]
            child2 = parent2[:2] + parent1[2:]
            population.extend([child1, child2])

        # 变异
        for i in range(population_size):
            if random.uniform(0, 1) < mutation_rate:
                chromosome = population[i]
                gene_index = random.randint(0, 3)
                if gene_index == 0:
                    chromosome[gene_index] += random.uniform(-1, 1)
                elif gene_index == 1:
                    chromosome[gene_index] += random.uniform(-1, 1)
                elif gene_index == 2:
                    chromosome[gene_index] += random.uniform(-np.pi / 8, np.pi / 8)
                else:
                    chromosome[gene_index] += random.uniform(-1, 1)

    # 返回最佳个体
    population_fitness = list(zip(population, fitness))
    population_fitness.sort(key=lambda x: x[1], reverse=True)
    best_chromosome = population_fitness[0][0]
    return best_chromosome

# 测试代码
x = 0
y = 0
theta = np.pi / 4
l = 100
target_x = 50
target_y = 50
best_chromosome = genetic_algorithm(population_size=100, mutation_rate=0.1, generations=50, x=x, y=y, theta=theta, l=l, target_x=target_x, target_y=target_y)
print(best_chromosome)
x, y, theta, l = best_chromosome
x2, y2 = trace_model(x, y, theta, l)
print(x2, y2)

在这个示例中，我们先定义了投影寻踪模型和适应度函数。然后使用遗传算法进行优化，最后输出最佳个体。你可以根据自己的需求修改代码，比如增加变异算子等。