如何根据上述代码,将每次优化迭代的结果输出并保存,给出代码
时间: 2024-03-09 11:47:06 浏览: 25
你可以在主函数中加入如下代码,实现在每次迭代后输出并保存优化结果:
```
def main():
# X坐标束条件
Min_pump_zcjj = 26
Max_pump_zcjj = 51
# Y坐标约束条件
Min_pump_bdljd = 26
Max_pump_bdljd = 51
# 靶点位置约束条件
Min_pump_bdwz = 0
Max_pump_bdwz = 1
# 射孔厚度约束条件
Min_pump_skhd = 1
Max_pump_skhd = 5
bounds = np.array([[Min_pump_zcjj, Max_pump_zcjj], [Min_pump_bdljd, Max_pump_bdljd], [Min_pump_bdwz, Max_pump_bdwz], [Min_pump_skhd, Max_pump_skhd]])
target = -(train_optimize2[19])
# 定义你的优化时间限制,输入是一个数值,表示你想要优化的最长时间(秒)
timeout = 300
# 生成初始种群
solutions = []
for _ in range(optimizer.population_size):
x = optimizer.ask()
x[0] = int(x[0])
x[1] = int(x[1])
if (x[0] == 1 and x[1] in [1, 51]) or (x[0] == 51 and x[1] in [1, 51]) or (x[0] == 26 and x[1] == 26):
pass
else:
value = quadratic(x[0], x[1], x[2], x[3])
if (x[0] == 1 and x[1] in [1, 51]) or (x[0] == 51 and x[1] in [1, 51]) or (x[0] == 26 and x[1] == 26):
pass
else:
solutions.append((x, value))
# 定义保存优化结果的数组
results = []
# 开始优化
while True:
# 将种群传递给CMA算法进行优化
optimizer.tell(solutions)
# 输出并保存当前迭代的最优解
best_x = optimizer.best.x
best_f = optimizer.best.f
print("Best solution - x = {}, f(x) = {}".format(best_x, best_f))
results.append((best_x, best_f))
# 生成新的种群
solutions = []
for _ in range(optimizer.population_size):
x = optimizer.ask()
x[0] = int(x[0])
x[1] = int(x[1])
if (x[0] == 1 and x[1] in [1, 51]) or (x[0] == 51 and x[1] in [1, 51]) or (x[0] == 26 and x[1] == 26):
pass
else:
value = quadratic(x[0], x[1], x[2], x[3])
if (x[0] == 1 and x[1] in [1, 51]) or (x[0] == 51 and x[1] in [1, 51]) or (x[0] == 26 and x[1] == 26):
pass
else:
solutions.append((x, value))
# 如果达到优化时间限制,则停止优化并保存结果
if optimizer.stop():
break
# 保存优化结果
np.save("optimization_results.npy", results)
```
在这里,我们使用一个while循环进行优化,并将生成的种群传递给CMA算法进行优化。在每次迭代后,输出当前的最优解(best_x和best_f),并将其保存在一个数组中。最后,使用numpy库的save函数将优化结果保存到一个npy文件中。你可以根据需要自己更改迭代次数和保存路径。