PSO 算法代码示例

粒子群优化（Particle Swarm Optimization，PSO）是一种优化算法，常用于解决连续优化问题。以下是一个简单的 PSO 算法代码示例。

代码结构

PSO 算法主要由以下几个部分组成：

• 粒子类：表示粒子，包含位置、速度、适应度等属性。
• 粒子群类：管理粒子群，包括初始化、更新、评估等操作。
• 优化目标函数：用于评估粒子的适应度。

代码示例

class Particle:
    def __init__(self, x, v, fitness):
        self.x = x
        self.v = v
        self.fitness = fitness

class ParticleSwarm:
    def __init__(self, num_particles, bounds, target):
        self.num_particles = num_particles
        self.bounds = bounds
        self.target = target
        self.particles = [self.create_particle() for _ in range(num_particles)]
        self.best_position = None
        self.best_fitness = float('inf')

    def create_particle(self):
        x = self.bounds[0] + (self.bounds[1] - self.bounds[0]) * random.random()
        v = self.bounds[0] + (self.bounds[1] - self.bounds[0]) * random.random()
        fitness = self.target(x)
        return Particle(x, v, fitness)

    def update_particles(self):
        for particle in self.particles:
            r1, r2 = random.random(), random.random()
            cognitive_component = r1 * (self.best_position - particle.x)
            social_component = r2 * (self.best_position - particle.x)
            particle.v = particle.v + cognitive_component + social_component
            particle.x = min(max(particle.x + particle.v, self.bounds[0]), self.bounds[1])
            particle.fitness = self.target(particle.x)

    def evaluate_particles(self):
        for particle in self.particles:
            particle.fitness = self.target(particle.x)

    def find_best(self):
        for particle in self.particles:
            if particle.fitness < self.best_fitness:
                self.best_fitness = particle.fitness
                self.best_position = particle.x

def target_function(x):
    return x * x

# 初始化粒子群
pso = ParticleSwarm(num_particles=30, bounds=(-10, 10), target=target_function)

# 运行优化
for _ in range(100):
    pso.update_particles()
    pso.evaluate_particles()
    pso.find_best()

print(f"Best position: {pso.best_position}, Best fitness: {pso.best_fitness}")

扩展阅读

更多关于 PSO 算法的介绍和实现，可以参考以下链接：

• 粒子群优化算法
• Python 中的粒子群优化算法实现

```python
import random

# 生成粒子群优化算法代码示例的图片
def generate_pso_images():
    keywords = ["Particle", "Swarm", "Optimization", "Algorithm", "Python", "Code"]
    images = []
    for keyword in keywords:
        images.append(f"<center><img src='https://cloud-image.ullrai.com/q/{keyword.replace(' ', '_')}/' alt='{keyword}'/></center>")
    return images

# 获取图片
pso_images = generate_pso_images()

# 在代码示例中插入图片
code_example_with_images = ""
for i, line in enumerate(open("pso_code_example.py", "r").readlines()):
    code_example_with_images += line
    if i == 10:  # 在代码示例的第 10 行插入第一张图片
        code_example_with_images += pso_images[0]
    elif i == 20:  # 在代码示例的第 20 行插入第二张图片
        code_example_with_images += pso_images[1]
    elif i == 30:  # 在代码示例的第 30 行插入第三张图片
        code_example_with_images += pso_images[2]
    elif i == 40:  # 在代码示例的第 40 行插入第四张图片
        code_example_with_images += pso_images[3]

# 保存修改后的代码示例
with open("pso_code_example_with_images.py", "w") as f:
    f.write(code_example_with_images)