蜣螂算法python

蜣螂算法（Firefly Algorithm）是一种启发式优化算法，模拟了萤火虫在寻找伴侣过程中的行为。它通过模拟萤火虫之间的相互吸引和斥力来搜索最优解。下面是一个用Python实现蜣螂算法的示例代码：

import math
import random

class FireflyAlgorithm:
    def __init__(self, problem_size, objective_func, lower_bound, upper_bound, population_size=50, alpha=0.5, beta0=1.0, gamma=1.0):
        self.problem_size = problem_size
        self.objective_func = objective_func
        self.lower_bound = lower_bound
        self.upper_bound = upper_bound
        self.population_size = population_size
        self.alpha = alpha
        self.beta0 = beta0
        self.gamma = gamma
        self.population = []

    def initialize_population(self):
        for _ in range(self.population_size):
            individual = [random.uniform(self.lower_bound, self.upper_bound) for _ in range(self.problem_size)]
            self.population.append(individual)

    def attractiveness(self, beta, distance):
        return math.exp(-beta * distance)

    def distance(self, x, y):
        return math.sqrt(sum((xi - yi) ** 2 for xi, yi in zip(x, y)))

    def move_fireflies(self):
        for i in range(self.population_size):
            for j in range(self.population_size):
                if self.objective_func(self.population[j]) < self.objective_func(self.population[i]):
                    distance = self.distance(self.population[i], self.population[j])
                    beta = self.beta0 * math.exp(-self.gamma * distance ** 2)
                    for k in range(self.problem_size):
                        self.population[i][k] += self.alpha * (self.population[j][k] - self.population[i][k]) + beta * random.uniform(-1, 1)

    def optimize(self, max_generations=100):
        self.initialize_population()
        for _ in range(max_generations):
            self.move_fireflies()
        best_solution = min(self.population, key=self.objective_func)
        return best_solution

# 使用示例
def sphere_function(x):
    return sum(xi ** 2 for xi in x)

algorithm = FireflyAlgorithm(problem_size=5, objective_func=sphere_function, lower_bound=-10, upper_bound=10)
best_solution = algorithm.optimize()
print("Best solution:", best_solution)

在上面的示例代码中，我们首先定义了一个`FireflyAlgorithm`类来实现蜣螂算法。在初始化方法中，我们传入问题的维度`problem_size`、目标函数`objective_func`、变量的取值范围`lower_bound`和`upper_bound`等参数。然后，我们使用`initialize_population`方法初始化种群，并使用`move_fireflies`方法模拟蜣螂的移动过程。最后，通过调用`optimize`方法，我们可以获取到找到的最优解。

在这个示例中，我们使用了经典的优化问题——球函数（Sphere Function）作为目标函数。你可以根据自己的需求来定义不同的目标函数和问题。