粒子群算法带注释的python代码

以下是粒子群算法的带注释的Python代码示例：

import random

class Particle:
    def __init__(self, x_min, x_max, dim):
        # 初始化粒子的位置和速度
        self.position = [random.uniform(x_min, x_max) for i in range(dim)]
        self.velocity = [0.0 for i in range(dim)]
        # 初始化个体最佳位置和适应值
        self.best_position = self.position[:]
        self.best_fitness = float('inf')

    def update_velocity(self, global_best_position, w, c1, c2):
        # 更新粒子的速度
        for i in range(len(self.position)):
            r1 = random.random()
            r2 = random.random()
            cognitive = c1 * r1 * (self.best_position[i] - self.position[i])
            social = c2 * r2 * (global_best_position[i] - self.position[i])
            self.velocity[i] = w * self.velocity[i] + cognitive + social

    def update_position(self, x_min, x_max):
        # 更新粒子的位置
        for i in range(len(self.position)):
            self.position[i] += self.velocity[i]
            # 确保粒子的位置在搜索空间内
            if self.position[i] < x_min:
                self.position[i] = x_min
            elif self.position[i] > x_max:
                self.position[i] = x_max

    def evaluate(self, fitness_func):
        # 计算粒子的适应值
        self.fitness = fitness_func(self.position)
        # 更新个体最佳位置和适应值
        if self.fitness < self.best_fitness:
            self.best_position = self.position[:]
            self.best_fitness = self.fitness

class PSO:
    def __init__(self, fitness_func, dim, size, x_min, x_max, w, c1, c2):
        # 初始化粒子群的参数
        self.fitness_func = fitness_func
        self.dim = dim
        self.size = size
        self.x_min = x_min
        self.x_max = x_max
        self.w = w
        self.c1 = c1
        self.c2 = c2
        # 初始化粒子群的粒子
        self.particles = [Particle(x_min, x_max, dim) for i in range(size)]
        # 初始化全局最佳位置和适应值
        self.global_best_position = self.particles[0].position[:]
        self.global_best_fitness = float('inf')

    def update(self, max_iter):
        # 粒子群更新迭代
        for i in range(max_iter):
            # 更新每个粒子的速度和位置
            for particle in self.particles:
                particle.update_velocity(self.global_best_position, self.w, self.c1, self.c2)
                particle.update_position(self.x_min, self.x_max)
                particle.evaluate(self.fitness_func)
                # 更新全局最佳位置和适应值
                if particle.fitness < self.global_best_fitness:
                    self.global_best_position = particle.position[:]
                    self.global_best_fitness = particle.fitness

            # 输出每一代的全局最佳适应值
            print(f'iter: {i}, best fitness: {self.global_best_fitness:.6f}')

        # 返回全局最佳位置和适应值
        return self.global_best_position, self.global_best_fitness

使用示例：

def fitness_func(x):
    # 适应值函数：Rastrigin函数
    return sum([(xi**2 - 10 * math.cos(2 * math.pi * xi)) for xi in x]) + 10 * len(x)

pso = PSO(fitness_func, dim=5, size=20, x_min=-5.12, x_max=5.12, w=0.8, c1=2.0, c2=2.0)
best_position, best_fitness = pso.update(max_iter=50)

print(f'best position: {best_position}')
print(f'best fitness: {best_fitness:.6f}')