m = n _x cosarctancosBtanA+R cosarctancosB tanA sinR x - 2D-09=x=-080=B=2pi-18520=n=18520A = 15度D = 110R = 60度如何使用遗传算法求解minm给出python代码

下面是使用遗传算法求解min(m)的Python代码：

import random
import math

def fitness_function(x, B, n):
    A = 1.5
    D = 110
    R = 60

    tanA = math.tan(math.radians(A))
    cosB = math.cos(B)
    sinR = math.sin(math.radians(R))

    numerator = n * x * math.cos(math.atan(cosB * tanA) + R)
    denominator = math.cos(math.atan(cosB * tanA) * sinR * x - 2 * D)

    m = numerator / denominator

    return m

def generate_population(population_size):
    population = []
    for _ in range(population_size):
        x = random.uniform(-0.9, -0.8)
        B = random.uniform(0, 2 * math.pi)
        n = random.randint(-18520, 18520)
        chromosome = [x, B, n]
        population.append(chromosome)
    return population

def selection(population, fitness_values):
    selected_population = []
    total_fitness = sum(fitness_values)
    probabilities = [fitness / total_fitness for fitness in fitness_values]
    cumulative_probabilities = [sum(probabilities[:i+1]) for i in range(len(probabilities))]

    for _ in range(len(population)):
        random_number = random.random()
        for i in range(len(cumulative_probabilities)):
            if random_number <= cumulative_probabilities[i]:
                selected_population.append(population[i])
                break

    return selected_population

def crossover(parent1, parent2):
    crossover_point = random.randint(1, len(parent1) - 2)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2

def mutation(chromosome, mutation_rate):
    mutated_chromosome = chromosome[:]
    for i in range(len(mutated_chromosome)):
        if random.random() < mutation_rate:
            if i == 0:
                mutated_chromosome[i] = random.uniform(-0.9, -0.8)
            elif i == 1:
                mutated_chromosome[i] = random.uniform(0, 2 * math.pi)
            else:
                mutated_chromosome[i] = random.randint(-18520, 18520)

    return mutated_chromosome

def genetic_algorithm(population_size, generations, mutation_rate):
    population = generate_population(population_size)

    for _ in range(generations):
        fitness_values = [fitness_function(chromosome[0], chromosome[1], chromosome[2]) for chromosome in population]

        selected_population = selection(population, fitness_values)

        new_population = []

        while len(new_population) < population_size:
            parent1 = random.choice(selected_population)
            parent2 = random.choice(selected_population)

            child1, child2 = crossover(parent1, parent2)

            mutated_child1 = mutation(child1, mutation_rate)
            mutated_child2 = mutation(child2, mutation_rate)

            new_population.extend([mutated_child1, mutated_child2])

        population = new_population

    best_chromosome = max(population, key=lambda chromosome: fitness_function(chromosome[0], chromosome[1], chromosome[2]))

    return best_chromosome

# 使用遗传算法求解min(m)
population_size = 100
generations = 100
mutation_rate = 0.1

best_chromosome = genetic_algorithm(population_size, generations, mutation_rate)

print("Best chromosome:", best_chromosome)
print("Minimum m:", fitness_function(best_chromosome[0], best_chromosome[1], best_chromosome[2]))

请注意，上述代码中的fitness_function函数计算给定染色体的适应度，generate_population函数生成初始种群，selection函数根据适应度选择父代，crossover函数进行交叉操作，mutation函数进行变异操作，genetic_algorithm函数是整个遗传算法的主要部分。您可以根据需要调整population_size、generations和mutation_rate等参数。最后，结果将输出最佳染色体以及对应的最小m值。