幫我撰寫一個數入變數為voltage = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100] current = [0.5, 1, 1.5, 1.8, 1.9, 1.8, 1.6, 1.4, 1.2, 0.8]的DPSO粒子群python代码
时间: 2024-09-23 13:09:45 浏览: 29
当然,这里是一个使用Differential Population Search Optimization (DPSO)算法的Python代码片段,用于解决电压和电流之间的优化问题,假设目标是最小化误差或最大化功率。请注意,这个例子假设我们想要找到一组电流值,使得它们产生的总功率尽可能接近给定的电压列表。实际应用中可能需要调整以适应具体的需求。
```python
import numpy as np
def fitness_function(current, voltage):
# 假设功率计算公式是电压乘以电流
power = np.multiply(voltage, current)
return sum(np.abs(power - voltage)) # 误差函数,尽量使功率接近电压
# 初始粒子群设置
population_size = 50
velocities = np.random.uniform(-1, 1, size=(population_size, len(current)))
positions = velocities # 粒子位置初始化等于速度
# 参数设定
w = 0.7 # 学习因子
c1, c2 = 2, 2 # 全局最佳和局部最佳影响权重
max_iter = 100 # 迭代次数
# DPSO算法核心循环
for iteration in range(max_iter):
for i in range(population_size):
# 更新全局最优解(g_best)
if fitness_function(positions[i], voltage) < fitness_function(particle_gbest_pos, voltage):
particle_gbest_pos = positions[i].copy()
g_best_fitness = fitness_function(positions[i], voltage)
# 更新局部最优解(p_best)
if fitness_function(positions[i], voltage) < fitness_function(particle_pbest_pos[i], voltage):
particle_pbest_pos[i] = positions[i].copy()
# 飞行规则更新
r1, r2 = np.random.rand(), np.random.rand() # 两个随机数
velocities[i] = w * velocities[i] + c1 * r1 * (particle_pbest_pos[i] - positions[i]) + \
c2 * r2 * (particle_gbest_pos[i] - positions[i])
# 移动粒子
positions[i] += velocities[i]
# 每一代结束后,打印当前迭代的全局最优结果
print(f"Iteration {iteration+1}: Best Fitness: {g_best_fitness}")
# 结果存储于g_best_pos
```
阅读全文