差分进化的适应性流控架构的python实现

以下是差分进化的适应性流控架构的Python实现示例：

import random
import numpy as np

# 定义差分进化算法
def DE(fobj, bounds, mut=1, crossp=0.7, popsize=20, maxiter=100):
    dimensions = len(bounds)
    pop = np.random.rand(popsize, dimensions)
    min_b, max_b = np.asarray(bounds).T
    diff = np.fabs(min_b - max_b)
    pop_denorm = min_b + pop * diff
    fitness = np.asarray([fobj(ind) for ind in pop_denorm])
    best_idx = np.argmin(fitness)
    best = pop_denorm[best_idx]
    for i in range(maxiter):
        for j in range(popsize):
            idxs = random.sample(range(popsize), 3)
            x_1, x_2, x_3 = pop[idxs]
            mutant = np.clip(x_1 + mut * (x_2 - x_3), 0, 1)
            cross_points = np.random.rand(dimensions) < crossp
            if not np.any(cross_points):
                cross_points[np.random.randint(0, dimensions)] = True
            trial = np.where(cross_points, mutant, pop[j])
            trial_denorm = min_b + trial * diff
            f = fobj(trial_denorm)
            if f < fitness[j]:
                fitness[j] = f
                pop[j] = trial
                if f < fitness[best_idx]:
                    best_idx = j
                    best = trial_denorm
    return best, fitness[best_idx]

# 定义适应性流控架构
def adaptive_flow_control(flow_rate, target_flow_rate, current_latency, target_latency, k=0.1):
    delta_flow_rate = target_flow_rate - flow_rate
    delta_latency = target_latency - current_latency
    control_signal = k * delta_flow_rate + delta_latency
    return control_signal

if __name__ == '__main__':
    # 定义目标函数
    def obj_func(x):
        flow_rate = x[0]
        target_flow_rate = x[1]
        current_latency = x[2]
        target_latency = x[3]
        return adaptive_flow_control(flow_rate, target_flow_rate, current_latency, target_latency)

    # 设置优化参数和变量范围
    bounds = [(0, 10), (0, 10), (0, 10), (0, 10)]
    mut = 1
    crossp = 0.7
    popsize = 20
    maxiter = 100

    # 进行优化
    best, fitness = DE(obj_func, bounds, mut, crossp, popsize, maxiter)

    # 输出结果
    print("最优解：", best)
    print("最优值：", fitness)

这个示例中，`DE()` 函数是差分进化算法的实现，`adaptive_flow_control()` 函数是适应性流控架构的实现。在主函数中，我们将 `DE()` 函数和 `adaptive_flow_control()` 函数结合起来，构建了一个优化函数 `obj_func()`，并按照指定的参数进行差分进化优化。最后输出优化结果。

注意，在实现中，需要根据具体的问题对差分进化算法和适应性流控架构进行相应的修改和优化。