蜂群算法 ，python实现

蜂群算法是一种基于模拟蜜蜂觅食行为的优化算法，可以用于解决一些优化问题，比如旅行商问题、函数优化等。下面是基于 Python 语言实现蜂群算法的示例代码：

import random
import math

class Bee:
    def __init__(self, problem_size):
        self.position = [random.uniform(-5, 5) for i in range(problem_size)]
        self.fitness = 0

class BeeColony:
    def __init__(self, problem_size, colony_size, max_iter):
        self.problem_size = problem_size
        self.colony_size = colony_size
        self.max_iter = max_iter
        self.best_solution = None
        self.best_fitness = float('inf')
        self.colony = [Bee(problem_size) for i in range(colony_size)]
        self.trial_limit = int(colony_size / 2)

    def evaluate(self, bee):
        bee.fitness = sum([bee.position[i] ** 2 for i in range(self.problem_size)])

    def send_employee_bees(self):
        for i in range(self.colony_size):
            index = random.randint(0, self.colony_size - 1)
            while index == i:
                index = random.randint(0, self.colony_size - 1)
            bee = Bee(self.problem_size)
            for j in range(self.problem_size):
                bee.position[j] = self.colony[i].position[j] + random.uniform(-1, 1) * (self.colony[i].position[j] - self.colony[index].position[j])
                if bee.position[j] < -5:
                    bee.position[j] = -5
                elif bee.position[j] > 5:
                    bee.position[j] = 5
            self.evaluate(bee)
            if bee.fitness < self.colony[i].fitness:
                self.colony[i] = bee
                self.colony[i].trials = 0
            else:
                self.colony[i].trials += 1

    def send_onlooker_bees(self):
        fitness_sum = sum([math.exp(-self.colony[i].fitness) for i in range(self.colony_size)])
        for i in range(self.colony_size):
            probability = math.exp(-self.colony[i].fitness) / fitness_sum
            if random.uniform(0, 1) < probability:
                index = random.randint(0, self.colony_size - 1)
                while index == i:
                    index = random.randint(0, self.colony_size - 1)
                bee = Bee(self.problem_size)
                for j in range(self.problem_size):
                    bee.position[j] = self.colony[i].position[j] + random.uniform(-1, 1) * (self.colony[i].position[j] - self.colony[index].position[j])
                    if bee.position[j] < -5:
                        bee.position[j] = -5
                    elif bee.position[j] > 5:
                        bee.position[j] = 5
                self.evaluate(bee)
                if bee.fitness < self.colony[i].fitness:
                    self.colony[i] = bee
                    self.colony[i].trials = 0
                else:
                    self.colony[i].trials += 1

    def send_scout_bees(self):
        for i in range(self.colony_size):
            if self.colony[i].trials > self.trial_limit:
                self.colony[i] = Bee(self.problem_size)
                self.evaluate(self.colony[i])

    def optimize(self):
        for i in range(self.colony_size):
            self.evaluate(self.colony[i])
            if self.colony[i].fitness < self.best_fitness:
                self.best_fitness = self.colony[i].fitness
                self.best_solution = self.colony[i].position
        iter = 0
        while iter < self.max_iter:
            self.send_employee_bees()
            self.send_onlooker_bees()
            self.send_scout_bees()
            for i in range(self.colony_size):
                if self.colony[i].fitness < self.best_fitness:
                    self.best_fitness = self.colony[i].fitness
                    self.best_solution = self.colony[i].position
            iter += 1
        return self.best_solution, self.best_fitness

在这个示例代码中，`Bee` 类表示一个蜜蜂，包含了实数向量 `position` 和适应度值 `fitness` 等属性。`BeeColony` 类表示整个蜂群，包含了问题的大小 `problem_size`，蜂群的大小 `colony_size`，最大迭代次数 `max_iter`，以及最优解和最优适应度等属性。`send_employee_bees`、`send_onlooker_bees` 和 `send_scout_bees` 分别表示蜜蜂的三种角色：员工蜂、观察蜂和侦查蜂。整个优化过程在 `optimize` 方法中实现，先进行初始化，然后进行迭代优化，最后返回最优解和最优适应度。