粒子群算法的python代码

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。下面是一个简单的粒子群算法的Python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):
        # 计算适应度函数值，这里以目标函数的值作为适应度
        x, y = self.position, self.position
        fitness = x**2 + y**2
        return fitness

class PSO:
    def __init__(self, num_particles, num_iterations, dim, w, c1, c2):
        self.num_particles = num_particles
        self.num_iterations = num_iterations
        self.dim = dim
        self.w = w
        self.c1 = c1
        self.c2 = c2
        self.global_best_position =  * dim
        self.global_best_fitness = float('inf')
        self.particles = [Particle(dim) for _ in range(num_particles)]

    def optimize(self):
        for _ in range(self.num_iterations):
            for particle in self.particles:
                fitness = particle.evaluate_fitness()
                if fitness < particle.best_fitness:
                    particle.best_fitness = fitness
                    particle.best_position = particle.position.copy()
                if fitness < self.global_best_fitness:
                    self.global_best_fitness = fitness
                    self.global_best_position = particle.position.copy()
            for particle in self.particles:
                particle.update_velocity(self.global_best_position, self.w, self.c1, self.c2)
                particle.update_position()

if __name__ == '__main__':
    num_particles = 30
    num_iterations = 100
    dim = 2
    w = 0.5
    c1 = 1
    c2 = 2

    pso = PSO(num_particles, num_iterations, dim, w, c1, c2)
    pso.optimize()
    print("Optimal solution:", pso.global_best_position)
    print("Optimal fitness:", pso.global_best_fitness)

这段代码实现了一个简单的粒子群算法，用于求解二维目标函数的最小值。你可以根据需要修改目标函数和算法参数来适应不同的问题。