粒子群算法python代码实例

粒子群算法（Particle Swarm Optimization，PSO）是一种优化算法，用于解决各种优化问题。下面是一个使用Python实现的粒子群算法的代码示例：

import random

class Particle:
    def __init__(self, dim):
        self.position = [random.uniform(-5, 5) for _ in range(dim)]
        self.velocity = [random.uniform(-1, 1) for _ in range(dim)]
        self.best_position = self.position.copy()
        self.best_fitness = float('inf')

    def update_velocity(self, global_best_position, w, c1, c2):
        for i in range(len(self.velocity)):
            r1 = random.random()
            r2 = random.random()
            cognitive_component = c1 * r1 * (self.best_position[i] - self.position[i])
            social_component = c2 * r2 * (global_best_position[i] - self.position[i])
            self.velocity[i] = w * self.velocity[i] + cognitive_component + social_component

    def update_position(self):
        for i in range(len(self.position)):
            self.position[i] += self.velocity[i]
            if self.position[i] < -5:
                self.position[i] = -5
            elif self.position[i] > 5:
                self.position[i] = 5

    def evaluate_fitness(self):
        # 这里根据具体的优化问题来定义适应度函数
        x, y = self.position[0], self.position[1]
        fitness = (x - 2) ** 2 + (y - 3) ** 2
        if fitness < self.best_fitness:
            self.best_fitness = fitness
            self.best_position = self.position.copy()

def particle_swarm_optimization(dim, num_particles, max_iterations):
    particles = [Particle(dim) for _ in range(num_particles)]
    global_best_position = None
    global_best_fitness = float('inf')

    for _ in range(max_iterations):
        for particle in particles:
            particle.evaluate_fitness()
            if particle.best_fitness < global_best_fitness:
                global_best_fitness = particle.best_fitness
                global_best_position = particle.best_position.copy()

        for particle in particles:
            particle.update_velocity(global_best_position, 0.5, 1, 2)
            particle.update_position()

    return global_best_position, global_best_fitness

# 示例使用二维空间中的粒子群算法来求解(x-2)^2 + (y-3)^2 的最小值
best_position, best_fitness = particle_swarm_optimization(2, 50, 100)
print("Best position:", best_position)
print("Best fitness:", best_fitness)

这段代码实现了一个简单的粒子群算法，用于求解二维空间中函数 (x-2)^2 + (y-3)^2 的最小值。代码中的参数可以根据具体问题进行调整。在每次迭代中，粒子根据自身的速度和位置更新策略来更新自己的位置，并根据新位置计算适应度值。全局最优解是所有粒子中适应度值最小的解。最后，算法返回全局最优解的位置和适应度值。