人工免疫算法解决f(x,y)=(a-x)^2+b(y-x^2)^2最优解的python代码

以下是使用人工免疫算法求解函数f(x,y)=(a-x)^2 b(y-x^2)^2最优解的python代码：

import random
import math

def calculate_fitness(x, y, a, b):
    return (a - x) ** 2 + b * (y - x ** 2) ** 2

def generate_antibody():
    return [random.uniform(-10.0, 10.0), random.uniform(-10.0, 10.0)]

def clone_antibody(antibody):
    clone = [x + random.gauss(0.0, 0.1) for x in antibody]
    return clone

def mutate_antibody(antibody, mutation_rate):
    mutated_antibody = []
    for x in antibody:
        if random.random() < mutation_rate:
            mutated_antibody.append(x + random.gauss(0.0, 1.0))
        else:
            mutated_antibody.append(x)
    return mutated_antibody

def select_antibodies(population, selection_rate):
    population_size = len(population)
    num_selected = int(population_size * selection_rate)
    population.sort(key=lambda x: x[1], reverse=True)
    return population[:num_selected]

def generate_population(num_antibodies):
    population = []
    for i in range(num_antibodies):
        antibody = generate_antibody()
        fitness = calculate_fitness(antibody[0], antibody[1], 1, 100)
        population.append((antibody, fitness))
    return population

def artificial_immune_algorithm(num_antibodies, num_iterations, selection_rate, mutation_rate):
    population = generate_population(num_antibodies)
    for i in range(num_iterations):
        clones = [clone_antibody(a) for a, _ in population]
        mutated = [mutate_antibody(c, mutation_rate) for c in clones]
        antibodies = population + [(m, calculate_fitness(m[0], m[1], 1, 100)) for m in mutated]
        selected = select_antibodies(antibodies, selection_rate)
        population = selected
        print('Iteration:', i + 1, 'Best fitness:', population[0][1])
    return population[0][0]

if __name__ == '__main__':
    best_antibody = artificial_immune_algorithm(50, 100, 0.2, 0.1)
    print('Best solution:', best_antibody)

请注意，此代码并不一定是最优解，因为人工免疫算法是一种随机化算法，每次运行可能会得到不同的结果。