叮叮车不是车 2022-09-03 23:49 采纳率: 20%
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已结题

python遗传算法


import numpy as np

DNA_size = 24
pops = 100
jiaohuan = 0.7
bianyi = 0.03
gen = 100
# x_bound=[-3,3]
# y_bound=[-3,3]
x1_bound = [0, 3147]
x2_bound = [0, 30977]
x3_bound = [0, 1724]
x4_bound = [0, 966]
x5_bound = [0, 971]
x6_bound = [0, 7661]
x7_bound = [0, 9385]
x8_bound = [0, 2521]
x9_bound = [0, 699]
x10_bound = [0, 36972]
x11_bound = [0, 7885]
x12_bound = [0, 10207]
x13_bound = [0, 1181]
x14_bound = [0, 9768]
x15_bound = [0, 8181]
x16_bound = [0, 1014]
x17_bound = [0, 21293]
x18_bound = [0, 2081]
x19_bound = [0, 2816]
x20_bound = [0, 21267]
x21_bound = [0, 1788]
x22_bound = [0, 736]
x23_bound = [0, 922]
x24_bound = [0, 595]
x25_bound = [0, 15114]
x26_bound = [0, 23695]
x27_bound = [0, 5398]
x28_bound = [0, 342]
x29_bound = [0, 2005]
x30_bound = [0, 381]


def fun(x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24,
        x25, x26, x27, x28, x29, x30):
    return 1.2 * (x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10) + 1.1 * (
            x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18 + x19 + x20) + (
                   x21 + x22 + x23 + x24 + x25 + x26 + x27 + x28 + x29 + x30)


def get_fitness(pop):
    x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24, x25, x26, x27, x28, x29, x30 = trans(
        pop)
    pred = fun(x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23,
               x24, x25, x26, x27, x28, x29, x30)
    return pred


def trans(pop):
    x1_pop = pop[:, 0:DNA_size]
    x2_pop = pop[:, 0:DNA_size]
    x3_pop = pop[:, 0:DNA_size]
    x4_pop = pop[:, 0:DNA_size]
    x5_pop = pop[:, 0:DNA_size]
    x6_pop = pop[:, 0:DNA_size]
    x7_pop = pop[:, 0:DNA_size]
    x8_pop = pop[:, 0:DNA_size]
    x9_pop = pop[:, 0:DNA_size]
    x10_pop = pop[:, 0:DNA_size]
    x11_pop = pop[:, 0:DNA_size]
    x12_pop = pop[:, 0:DNA_size]
    x13_pop = pop[:, 0:DNA_size]
    x14_pop = pop[:, 0:DNA_size]
    x15_pop = pop[:, 0:DNA_size]
    x16_pop = pop[:, 0:DNA_size]
    x17_pop = pop[:, 0:DNA_size]
    x18_pop = pop[:, 0:DNA_size]
    x19_pop = pop[:, 0:DNA_size]
    x20_pop = pop[:, 0:DNA_size]
    x21_pop = pop[:, 0:DNA_size]
    x22_pop = pop[:, 0:DNA_size]
    x23_pop = pop[:, 0:DNA_size]
    x24_pop = pop[:, 0:DNA_size]
    x25_pop = pop[:, 0:DNA_size]
    x26_pop = pop[:, 0:DNA_size]
    x27_pop = pop[:, 0:DNA_size]
    x28_pop = pop[:, 0:DNA_size]
    x29_pop = pop[:, 0:DNA_size]
    x30_pop = pop[:, 0:DNA_size]

    x1 = x1_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x1_bound[1] - x1_bound[0]) + x1_bound[
        0]
    x2 = x2_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x2_bound[1] - x2_bound[0]) + x2_bound[
        0]
    x3 = x3_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x3_bound[1] - x3_bound[0]) + x3_bound[
        0]
    x4 = x4_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x4_bound[1] - x4_bound[0]) + x4_bound[
        0]
    x5 = x5_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x5_bound[1] - x5_bound[0]) + x5_bound[
        0]
    x6 = x6_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x6_bound[1] - x6_bound[0]) + x6_bound[
        0]
    x7 = x7_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x7_bound[1] - x7_bound[0]) + x7_bound[
        0]
    x8 = x8_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x8_bound[1] - x8_bound[0]) + x8_bound[
        0]
    x9 = x9_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x9_bound[1] - x9_bound[0]) + x9_bound[
        0]
    x10 = x10_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x10_bound[1] - x10_bound[0]) + \
          x10_bound[0]
    x11 = x11_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x11_bound[1] - x11_bound[0]) + \
          x11_bound[0]
    x12 = x12_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x12_bound[1] - x12_bound[0]) + \
          x12_bound[0]
    x13 = x13_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x13_bound[1] - x13_bound[0]) + \
          x13_bound[0]
    x14 = x14_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x14_bound[1] - x14_bound[0]) + \
          x14_bound[0]
    x15 = x15_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x15_bound[1] - x15_bound[0]) + \
          x15_bound[0]
    x16 = x16_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x16_bound[1] - x16_bound[0]) + \
          x16_bound[0]
    x17 = x17_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x17_bound[1] - x17_bound[0]) + \
          x17_bound[0]
    x18 = x18_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x18_bound[1] - x18_bound[0]) + \
          x18_bound[0]
    x19 = x19_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x19_bound[1] - x19_bound[0]) + \
          x19_bound[0]
    x20 = x20_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x20_bound[1] - x20_bound[0]) + \
          x20_bound[0]
    x21 = x21_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x21_bound[1] - x21_bound[0]) + \
          x21_bound[0]
    x22 = x22_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x22_bound[1] - x22_bound[0]) + \
          x22_bound[0]
    x23 = x23_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x23_bound[1] - x23_bound[0]) + \
          x23_bound[0]
    x24 = x24_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x24_bound[1] - x24_bound[0]) + \
          x24_bound[0]
    x25 = x25_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x25_bound[1] - x25_bound[0]) + \
          x25_bound[0]
    x26 = x26_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x26_bound[1] - x26_bound[0]) + \
          x26_bound[0]
    x27 = x27_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x27_bound[1] - x27_bound[0]) + \
          x27_bound[0]
    x28 = x28_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x28_bound[1] - x28_bound[0]) + \
          x28_bound[0]
    x29 = x29_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x29_bound[1] - x29_bound[0]) + \
          x29_bound[0]
    x30 = x30_pop.dot(2 ** np.arange(DNA_size)[::-1]) / float(2 ** DNA_size - 1) * (x30_bound[1] - x30_bound[0]) + \
          x30_bound[0]

    return x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24, x25, x26, x27, x28, x29, x30


def crossover(pop):
    new_pop = []
    pred = get_fitness(pop)
    index = np.argmax(pred)
    index2 = np.argmin(pred)
    for father in pop:
        if (father == pop[index2]).all() == True:
            child = pop[index]
        else:
            child = father
        if np.random.rand() < jiaohuan:
            mother = pop[np.random.randint(pops)]
            cross_point = np.random.randint(low=0, high=DNA_size * 2)
            child[cross_point:] = mother[cross_point:]
        child = mutation(child)
        new_pop.append(child)
    return new_pop


def mutation(child):
    if np.random.rand() < bianyi:
        mutate_point = np.random.randint(0, DNA_size)
        child[mutate_point] = child[mutate_point] ^ 1
    return child


def select(pop, fitness):
    x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24, x25, x26, x27, x28, x29, x30 = trans(
        pop)
    if (x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10) / 0.6 + (
            x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18 + x19 + x20) / 0.66 + (
            x21 + x22 + x23 + x24 + x25 + x26 + x27 + x28 + x29 + x30) / 0.72 <= 56400:
        if (x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10) <= 4642:
            if (x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18 + x19 + x20) <= 23833:
                if (x21 + x22 + x23 + x24 + x25 + x26 + x27 + x28 + x29 + x30) <= 8155:
                    idx = np.random.choice(np.arange(pops), size=pops, replace=True,
                                           p=(fitness) / (fitness.sum()))
                    return pop[idx]


def print_info(pop):
    fitness = get_fitness(pop)
    max_index = np.argmax(fitness)
    print("最优解：", fitness[max_index])
    x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24, x25, x26, x27, x28, x29, x30 = trans(
        pop)
    print("最优基因型：", pop[max_index])
    print("最优解的x,y :", x1[max_index], x2[max_index], x3[max_index], x4[max_index], x5[max_index], x6[max_index],
          x7[max_index], x8[max_index], x9[max_index], x10[max_index], x11[max_index], x12[max_index], x13[max_index],
          x14[max_index], x15[max_index], x16[max_index], x17[max_index], x18[max_index], x19[max_index],
          x20[max_index], x21[max_index], x22[max_index], x23[max_index], x24[max_index], x25[max_index],
          x26[max_index], x27[max_index], x28[max_index], x29[max_index], x30[max_index])


if __name__ == "__main__":
    pop = np.random.randint(2, size=(pops, DNA_size * 2))
    fitness = get_fitness(pop)
    for i in range(gen):
        pop = np.array(crossover(pop))
        fitness = get_fitness(pop)
        pop = select(pop, fitness)
    print_info(pop)

我想加入约束条件，是应该加到select函数里吗

img

为什么会报这样的错啊

img

  • 写回答

1条回答 默认 最新

  • 亖夕 Python领域新星创作者 2022-09-04 18:24
    关注

    值错误:包含一个以上元素的数组的真值是不明确的，要使用a.any()或a.all()
    183行改成
    if ((x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10) / 0.6 + (x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18 + x19 + x20) / 0.66 + (x21 + x22 + x23 + x24 + x25 + x26 + x27 + x28 + x29 + x30) / 0.72 <= 56400).all():

    本回答被题主选为最佳回答 , 对您是否有帮助呢?
    评论

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  • 系统已结题 9月16日
  • 已采纳回答 9月8日
  • 创建了问题 9月3日

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