粒子群算法求解车间调度问题python代码

好的，我可以给你一份 Python 代码，用粒子群算法解决车间调度问题：

import random

class PSO:
    def __init__(self, iterations, swarm_size, particles_dim, max_velocity):
        self.iterations = iterations
        self.swarm_size = swarm_size
        self.particles_dim = particles_dim
        self.max_velocity = max_velocity
        self.w = 0.729
        self.c1 = 1.49445
        self.c2 = 1.49445
        
        self.gbest_fitness = None
        self.gbest_position = None
        
        self.swarm_position = []  
        self.swarm_velocity = []  
        self.pbest_position = []  
        self.pbest_fitness = []  
        
        for i in range(swarm_size):
            position = [random.randint(0, particles_dim - 1) for i in range(particles_dim)]
            velocity = [random.uniform(-max_velocity, max_velocity) for i in range(particles_dim)]
            
            self.swarm_position.append(position)
            self.swarm_velocity.append(velocity)
            self.pbest_position.append(position)
            self.pbest_fitness.append(float('inf'))
            
    def run(self, eval_func):
        for i in range(self.iterations):
            for j in range(self.swarm_size):
                fitness = eval_func(self.swarm_position[j])
                
                if fitness < self.pbest_fitness[j]:
                    self.pbest_fitness[j] = fitness
                    self.pbest_position[j] = self.swarm_position[j]
                
                if self.gbest_fitness == None or fitness < self.gbest_fitness:
                    self.gbest_fitness = fitness
                    self.gbest_position = self.swarm_position[j]
                    
            for j in range(self.swarm_size):
                for k in range(self.particles_dim):
                    r1 = random.random()
                    r2 = random.random()
                    
                    vel_cognitive = self.c1 * r1 * (self.pbest_position[j][k] - self.swarm_position[j][k])
                    vel_social = self.c2 * r2 * (self.gbest_position[k] - self.swarm_position[j][k])
                    self.swarm_velocity[j][k] = self.w * self.swarm_velocity[j][k] + vel_cognitive + vel_social
                    
                    if self.swarm_velocity[j][k] > self.max_velocity:
                        self.swarm_velocity[j][k] = self.max_velocity
                    elif self.swarm_velocity[j][k] < -self.max_velocity:
                        self.swarm_velocity[j][k] = -self.max_velocity
                        
                    self.swarm_position[j][k] += self.swarm_velocity[j][k]
                    
        return self.gbest_fitness, self.gbest_position

这里是一个简单的使用案例：

def eval_func(position):
    # 对 position 进行车间调度，计算 makespan，并返回
    return makespan

pso = PSO(iterations=100, swarm_size=50, particles_dim=10, max_velocity=5)
best_fitness, best_position = pso.run(eval_func)
print('最短完成时间为：', best_fitness)
print('最佳车间调度为：', best_position)

你需要自己编写 eval_func 函数来计算 makespan。注意，这里用到的是“粒子群算法（Particle Swarm Optimization，PSO）”，不是“蚁群算法（Ant Colony Optimization，ACO）”。如果你需要 ACO 的相关代码的话，请告诉我。