启发式与元启发式（GA / SA / PSO）

# 依赖: scipy
import numpy as np
from scipy.optimize import differential_evolution

def objective(x):
    return float(np.sum(x ** 2) + 10.0 * np.sin(2.0 * np.pi * x[0]))

results = []
for seed in range(10):                      # 必须多跑几次报告稳定性
    r = differential_evolution(objective, bounds=[(-5, 5), (-5, 5)], seed=seed, polish=True)
    results.append(r.fun)

f = np.array(results)
print(f'最优={f.min():.4f} 均值={f.mean():.4f} 标准差={f.std():.4f}')