鸡群算法python代码

以下是一个简单的鸡群算法的 Python 代码实现：

import random

# 鸡群算法的参数
POPULATION_SIZE = 20  # 种群大小
DIMENSIONS = 2  # 搜索空间的维度
ITERATIONS = 100  # 迭代次数
C1 = 0.5  # 学习因子1
C2 = 0.5  # 学习因子2
W = 0.9  # 惯性权重

# 定义搜索空间
BOUND_LOW, BOUND_UP = -10, 10

# 初始化种群
class Chicken:
    def __init__(self):
        self.position = [random.uniform(BOUND_LOW, BOUND_UP) for _ in range(DIMENSIONS)]
        self.velocity = [random.uniform(BOUND_LOW, BOUND_UP) for _ in range(DIMENSIONS)]
        self.fitness = float("inf")
        self.best_position = self.position.copy()
        self.best_fitness = float("inf")

    def evaluate(self, cost_function):
        self.fitness = cost_function(self.position)
        if self.fitness < self.best_fitness:
            self.best_position = self.position.copy()
            self.best_fitness = self.fitness

class ChickenSwarm:
    def __init__(self, cost_function):
        self.cost_function = cost_function
        self.population = [Chicken() for _ in range(POPULATION_SIZE)]
        self.best_chicken = min(self.population, key=lambda chicken: chicken.fitness)

    def optimize(self):
        for i in range(ITERATIONS):
            for chicken in self.population:
                # 更新速度和位置
                for j in range(DIMENSIONS):
                    r1, r2 = random.random(), random.random()
                    chicken.velocity[j] = W * chicken.velocity[j] + \
                                          C1 * r1 * (chicken.best_position[j] - chicken.position[j]) + \
                                          C2 * r2 * (self.best_chicken.best_position[j] - chicken.position[j])
                    chicken.position[j] += chicken.velocity[j]

                    # 确保位置在搜索空间内
                    if chicken.position[j] < BOUND_LOW:
                        chicken.position[j] = BOUND_LOW
                    elif chicken.position[j] > BOUND_UP:
                        chicken.position[j] = BOUND_UP

                # 计算适应度
                chicken.evaluate(self.cost_function)

                # 更新全局最优解
                if chicken.fitness < self.best_chicken.fitness:
                    self.best_chicken = chicken

            print(f"Iteration {i+1}: Best fitness = {self.best_chicken.best_fitness}")

        return self.best_chicken.best_position

# 测试鸡群算法
def sphere_function(x):
    return sum([xi**2 for xi in x])

swarm = ChickenSwarm(sphere_function)
best_position = swarm.optimize()
print(f"Best position: {best_position}")