将pso.py和传递函数的代码整理

下面是整理后的 `pso.py` 代码和计算传递函数适应度的函数：

import numpy as np

class Particle:
    def __init__(self, dim):
        self.position = np.random.uniform(-1, 1, dim)
        self.velocity = np.zeros(dim)
        self.best_position = self.position
        self.best_fitness = float('inf')

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

    def optimize(self):
        for _ in range(self.max_iter):
            for particle in self.particles:
                fitness = self.evaluate_fitness(particle.position)
                if fitness < particle.best_fitness:
                    particle.best_position = particle.position
                    particle.best_fitness = fitness
                if fitness < self.global_best_fitness:
                    self.global_best_position = particle.position
                    self.global_best_fitness = fitness
                particle.velocity = (self.w * particle.velocity +
                                     self.c1 * np.random.random() * (particle.best_position - particle.position) +
                                     self.c2 * np.random.random() * (self.global_best_position - particle.position))
                particle.position += particle.velocity

    def evaluate_fitness(self, position):
        # 假设传递函数为 1/s^2 + s + 1
        Ts = 0.01
        total_time = 10
        target_output = 1

        state = 0
        fitness = 0

        for _ in range(int(total_time / Ts)):
            error = target_output - state
            control_signal = position[0] * error + position[1] * (error / Ts) + position[2] * (error * Ts)
            state += (control_signal - state) / (Ts * Ts + 2)
            fitness += abs(target_output - state)

        return fitness

# 使用示例
num_particles = 10
dim = 3  # PID参数的维度
max_iter = 100
c1 = 2.0
c2 = 2.0
w = 0.7

pso = PSO(num_particles, dim, max_iter, c1, c2, w)
pso.optimize()
best_pid_params = pso.global_best_position
print("Best PID parameters:", best_pid_params)

在这个示例中，我们将传递函数 `1/s^2 + s + 1` 的适应度计算函数整合到了 `pso.py` 中，并在 `evaluate_fitness` 方法中进行了计算。你可以根据需要修改传递函数以及其他参数。希望对你有所帮助！