能写出这个算法的python代码吗

当然可以，以下是一份简单的Python代码实现：

import numpy as np

def DE_with_Strategy_Adaptation(fobj, bounds, npop=50, F=0.5, CR=0.8, ngen=100):
    # 初始化种群
    pop = np.random.uniform(bounds[0], bounds[1], (npop, len(bounds)))
    # 初始化适应度
    fitness = np.array([fobj(ind) for ind in pop])
    # 初始化最优解和适应度
    best_idx = np.argmin(fitness)
    best = pop[best_idx]
    fbest = fitness[best_idx]

    for i in range(ngen):
        # 自适应调整F和CR
        if i > 0:
            F = np.mean(F_history[-3:])
            CR = np.mean(CR_history[-3:])
        
        # 选择变异策略
        if i > 0:
            if np.random.rand() < 0.1:
                strategy = np.random.choice(['best', 'rand', 'current'])
            else:
                strategy = strategy_history[-1]
        else:
            strategy = 'best'

        # 变异
        if strategy == 'best':
            idxs = np.random.choice(npop, 3, replace=False)
            x0, x1, x2 = pop[idxs]
            v = best + F * (x1 - x2)
        elif strategy == 'rand':
            idxs = np.random.choice(npop, 3, replace=False)
            x0, x1, x2 = pop[idxs]
            v = x0 + F * (x1 - x2)
        else:
            idx = np.random.randint(npop)
            x0 = pop[idx]
            idxs = np.random.choice(npop, 2, replace=False)
            x1, x2 = pop[idxs]
            v = x0 + F * (x1 - x2)
        
        # 交叉
        mask = np.random.rand(len(bounds)) < CR
        mask[np.random.randint(len(bounds))] = True
        u = np.where(mask, v, pop[i])

        # 选择
        fitness_u = fobj(u)
        if fitness_u < fitness[i]:
            pop[i] = u
            fitness[i] = fitness_u
            if fitness_u < fbest:
                best = u
                fbest = fitness_u

        # 记录历史
        F_history.append(F)
        CR_history.append(CR)
        strategy_history.append(strategy)

    return best, fbest

这份代码实现了DE算法的主要流程，包括种群初始化、变异、交叉、选择、自适应调整等步骤，并且加入了策略自适应机制。其中，`fobj`表示目标函数，`bounds`表示变量的上下界，`npop`表示种群大小，`F`和`CR`表示变异和交叉的参数，`ngen`表示迭代次数。代码中还记录了每次迭代的F、CR和策略，方便观察算法的收敛情况。