optimization.py 文件源码

def annealing_optimize(domain, cost_fuc, t=10000.0, cool=0.95, step=1):
    vec = [float(random.randint(domain[i][0], domain[i][1])) for i in range(len(domain))]

    while t > 0.1:
        i = random.randint(0, len(domain)-1)

        direction = random.randint(-step, step)

        new_vec = vec[:]
        new_vec[i] += direction
        if new_vec[i] < domain[i][0]:
            new_vec[i] = domain[i][0]
        elif new_vec[i] > domain[i][1]:
            new_vec[i] = domain[i][1]

        cost = cost_fuc(vec)
        new_cost = cost_fuc(new_vec)
        # ?? pow(math.e, -(new_cost-cost)/t) ???????
        # ?new_cost > cost ?????t????????new_cost
        # ??t????????????new_cost
        # ??t???????????new_cost
        if new_cost < cost or random.random() < pow(math.e, -(new_cost+cost)/t):
            vec = new_vec
        t *= cool
    return vec, cost_fuc(vec)