python实现粒子群算法

粒子群算法（Particle Swarm Optimization，PSO）是一种基于群体智能的优化算法，常用于解决优化问题。下面是一个使用Python实现粒子群算法的简单示例：

import random

class Particle:
    def __init__(self, dim, min_val, max_val):
        self.position = [random.uniform(min_val, max_val) 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 = 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, min_val, max_val):
        for i in range(len(self.position)):
            self.position[i] += self.velocity[i]
            if self.position[i] < min_val:
                self.position[i] = min_val
            elif self.position[i] > max_val:
                self.position[i] = max_val

    def evaluate_fitness(self, fitness_func):
        fitness = fitness_func(self.position)
        if fitness < self.best_fitness:
            self.best_fitness = fitness
            self.best_position = self.position.copy()

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

    for _ in range(num_iterations):
        for particle in particles:
            particle.evaluate_fitness(fitness_func)
            if particle.best_fitness < global_best_fitness:
                global_best_fitness = particle.best_fitness