关于react.js:一个易用易部署的Python遗传算法库

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简介： # [scikit-opt](https://github.com/guofei9987… [![PyPI](https://img.shields.io/pypi/v…](https://pypi.org/project/scik… [![release](https://img.shields.io/github…

一个封装了 7 种启发式算法的 Python 代码库
（差分进化算法、遗传算法、粒子群算法、模拟退火算法、蚁群算法、鱼群算法、免疫优化算法）

pip install scikit-opt

或者间接把源代码中的 sko 文件夹下载下来放本地也调用能够

举例来说，你想出一种新的“抉择算子”，如下
-> Demo code: examples/demo_ga_udf.py#s1

# step1: define your own operator:
def selection_tournament(algorithm, tourn_size):
    FitV = algorithm.FitV
    sel_index = []
    for i in range(algorithm.size_pop):
        aspirants_index = np.random.choice(range(algorithm.size_pop), size=tourn_size)
        sel_index.append(max(aspirants_index, key=lambda i: FitV[i]))
    algorithm.Chrom = algorithm.Chrom[sel_index, :]  # next generation
    return algorithm.Chrom

导入包，并且创立遗传算法实例
-> Demo code: examples/demo_ga_udf.py#s2

import numpy as np
from sko.GA import GA, GA_TSP

demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2
ga = GA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, lb=[-1, -10, -5], ub=[2, 10, 2],
        precision=[1e-7, 1e-7, 1])

把你的算子注册到你创立好的遗传算法实例上
-> Demo code: examples/demo_ga_udf.py#s3

ga.register(operator_name='selection', operator=selection_tournament, tourn_size=3)

scikit-opt 也提供了十几个算子供你调用
-> Demo code: examples/demo_ga_udf.py#s4

from sko.operators import ranking, selection, crossover, mutation

ga.register(operator_name='ranking', operator=ranking.ranking). \
    register(operator_name='crossover', operator=crossover.crossover_2point). \
    register(operator_name='mutation', operator=mutation.mutation)

做遗传算法运算
-> Demo code: examples/demo_ga_udf.py#s5

best_x, best_y = ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)

当初 udf 反对遗传算法的这几个算子：crossover, mutation, selection, ranking

Scikit-opt 也提供了十来个算子，参考这里

提供一个面向对象格调的自定义算子的办法，供进阶用户应用:

-> Demo code: examples/demo_ga_udf.py#s6

class MyGA(GA):
    def selection(self, tourn_size=3):
        FitV = self.FitV
        sel_index = []
        for i in range(self.size_pop):
            aspirants_index = np.random.choice(range(self.size_pop), size=tourn_size)
            sel_index.append(max(aspirants_index, key=lambda i: FitV[i]))
        self.Chrom = self.Chrom[sel_index, :]  # next generation
        return self.Chrom

    ranking = ranking.ranking


demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + (x[2] - 0.5) ** 2
my_ga = MyGA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, lb=[-1, -10, -5], ub=[2, 10, 2],
        precision=[1e-7, 1e-7, 1])
best_x, best_y = my_ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)

GPU 减速性能还比较简单，将会在 1.0.0 版本大大欠缺。
有个 demo 曾经能够在现版本运行了: https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py

例如，先跑 10 代，而后在此基础上再跑 20 代，能够这么写：

from sko.GA import GA

func = lambda x: x[0] ** 2
ga = GA(func=func, n_dim=1)
ga.run(10)
ga.run(20)

Step1：定义你的问题，这个 demo 定义了有束缚优化问题
-> Demo code: examples/demo_de.py#s1

'''
min f(x1, x2, x3) = x1^2 + x2^2 + x3^2
s.t.
    x1*x2 >= 1
    x1*x2 <= 5
    x2 + x3 = 1
    0 <= x1, x2, x3 <= 5
'''


def obj_func(p):
    x1, x2, x3 = p
    return x1 ** 2 + x2 ** 2 + x3 ** 2


constraint_eq = [lambda x: 1 - x[1] - x[2]
]

constraint_ueq = [lambda x: 1 - x[0] * x[1],
    lambda x: x[0] * x[1] - 5
]

Step2: 做差分进化算法
-> Demo code: examples/demo_de.py#s2

from sko.DE import DE

de = DE(func=obj_func, n_dim=3, size_pop=50, max_iter=800, lb=[0, 0, 0], ub=[5, 5, 5],
        constraint_eq=constraint_eq, constraint_ueq=constraint_ueq)

best_x, best_y = de.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)

第一步：定义你的问题
-> Demo code: examples/demo_ga.py#s1

import numpy as np


def schaffer(p):
    '''
    This function has plenty of local minimum, with strong shocks
    global minimum at (0,0) with value 0
    '''
    x1, x2 = p
    x = np.square(x1) + np.square(x2)
    return 0.5 + (np.sin(x) - 0.5) / np.square(1 + 0.001 * x)

第二步：运行遗传算法
-> Demo code: examples/demo_ga.py#s2

from sko.GA import GA

ga = GA(func=schaffer, n_dim=2, size_pop=50, max_iter=800, lb=[-1, -1], ub=[1, 1], precision=1e-7)
best_x, best_y = ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)

第三步：用 matplotlib 画出后果
-> Demo code: examples/demo_ga.py#s3

import pandas as pd
import matplotlib.pyplot as plt

Y_history = pd.DataFrame(ga.all_history_Y)
fig, ax = plt.subplots(2, 1)
ax[0].plot(Y_history.index, Y_history.values, '.', color='red')
Y_history.min(axis=1).cummin().plot(kind='line')
plt.show()

GA_TSP 针对 TSP 问题重载了 穿插 (crossover)、 变异(mutation) 两个算子

第一步 ，定义问题。
这里作为 demo，随机生成间隔矩阵. 实战中从实在数据源中读取。

-> Demo code: examples/demo_ga_tsp.py#s1

import numpy as np
from scipy import spatial
import matplotlib.pyplot as plt

num_points = 50

points_coordinate = np.random.rand(num_points, 2)  # generate coordinate of points
distance_matrix = spatial.distance.cdist(points_coordinate, points_coordinate, metric='euclidean')


def cal_total_distance(routine):
    '''The objective function. input routine, return total distance.
    cal_total_distance(np.arange(num_points))
    '''
    num_points, = routine.shape
    return sum([distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]] for i in range(num_points)])

第二步，调用遗传算法进行求解
-> Demo code: examples/demo_ga_tsp.py#s2


from sko.GA import GA_TSP

ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=50, max_iter=500, prob_mut=1)
best_points, best_distance = ga_tsp.run()

第三步，画出后果：
-> Demo code: examples/demo_ga_tsp.py#s3

fig, ax = plt.subplots(1, 2)
best_points_ = np.concatenate([best_points, [best_points[0]]])
best_points_coordinate = points_coordinate[best_points_, :]
ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')
ax[1].plot(ga_tsp.generation_best_Y)
plt.show()

(PSO, Particle swarm optimization)

第一步，定义问题
-> Demo code: examples/demo_pso.py#s1

def demo_func(x):
    x1, x2, x3 = x
    return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2

第二步，做粒子群算法
-> Demo code: examples/demo_pso.py#s2

from sko.PSO import PSO

pso = PSO(func=demo_func, dim=3, pop=40, max_iter=150, lb=[0, -1, 0.5], ub=[1, 1, 1], w=0.8, c1=0.5, c2=0.5)
pso.run()
print('best_x is', pso.gbest_x, 'best_y is', pso.gbest_y)

第三步，画出后果
-> Demo code: examples/demo_pso.py#s3

import matplotlib.pyplot as plt

plt.plot(pso.gbest_y_hist)
plt.show()

↑see examples/demo_pso.py

-> Demo code: examples/demo_pso.py#s4

pso = PSO(func=demo_func, dim=3)
fitness = pso.run()
print('best_x is', pso.gbest_x, 'best_y is', pso.gbest_y)

(SA, Simulated Annealing)

第一步：定义问题
-> Demo code: examples/demo_sa.py#s1

demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2

第二步，运行模拟退火算法
-> Demo code: examples/demo_sa.py#s2

from sko.SA import SA

sa = SA(func=demo_func, x0=[1, 1, 1], T_max=1, T_min=1e-9, L=300, max_stay_counter=150)
best_x, best_y = sa.run()
print('best_x:', best_x, 'best_y', best_y)

第三步，画出后果
-> Demo code: examples/demo_sa.py#s3

import matplotlib.pyplot as plt
import pandas as pd

plt.plot(pd.DataFrame(sa.best_y_history).cummin(axis=0))
plt.show()

另外，scikit-opt 还提供了三种模拟退火流派: Fast, Boltzmann, Cauchy. 更多参见 more sa

第一步，定义问题。（我猜你曾经无聊了，所以不黏贴这一步了）

第二步，调用模拟退火算法
-> Demo code: examples/demo_sa_tsp.py#s2

from sko.SA import SA_TSP

sa_tsp = SA_TSP(func=cal_total_distance, x0=range(num_points), T_max=100, T_min=1, L=10 * num_points)

best_points, best_distance = sa_tsp.run()
print(best_points, best_distance, cal_total_distance(best_points))

第三步，画出后果
-> Demo code: examples/demo_sa_tsp.py#s3

from matplotlib.ticker import FormatStrFormatter

fig, ax = plt.subplots(1, 2)

best_points_ = np.concatenate([best_points, [best_points[0]]])
best_points_coordinate = points_coordinate[best_points_, :]
ax[0].plot(sa_tsp.best_y_history)
ax[0].set_xlabel("Iteration")
ax[0].set_ylabel("Distance")
ax[1].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1],
           marker='o', markerfacecolor='b', color='c', linestyle='-')
ax[1].xaxis.set_major_formatter(FormatStrFormatter('%.3f'))
ax[1].yaxis.set_major_formatter(FormatStrFormatter('%.3f'))
ax[1].set_xlabel("Longitude")
ax[1].set_ylabel("Latitude")
plt.show()

咱还有个动画

↑参考代码 examples/demo_sa_tsp.py

蚁群算法 (ACA, Ant Colony Algorithm) 解决 TSP 问题

-> Demo code: examples/demo_aca_tsp.py#s2

from sko.ACA import ACA_TSP

aca = ACA_TSP(func=cal_total_distance, n_dim=num_points,
              size_pop=50, max_iter=200,
              distance_matrix=distance_matrix)

best_x, best_y = aca.run()

(immune algorithm, IA)
-> Demo code: examples/demo_ia.py#s2


from sko.IA import IA_TSP

ia_tsp = IA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=500, max_iter=800, prob_mut=0.2,
                T=0.7, alpha=0.95)
best_points, best_distance = ia_tsp.run()
print('best routine:', best_points, 'best_distance:', best_distance)

人工鱼群算法(artificial fish swarm algorithm, AFSA)

-> Demo code: examples/demo_afsa.py#s1

def func(x):
    x1, x2 = x
    return 1 / x1 ** 2 + x1 ** 2 + 1 / x2 ** 2 + x2 ** 2


from sko.AFSA import AFSA

afsa = AFSA(func, n_dim=2, size_pop=50, max_iter=300,
            max_try_num=100, step=0.5, visual=0.3,
            q=0.98, delta=0.5)
best_x, best_y = afsa.run()
print(best_x, best_y)

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本文为阿里云原创内容，未经容许不得转载。

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关于react.js:一个易用易部署的Python遗传算法库

scikit-opt

装置

个性

个性 1：UDF（用户自定义算子）

个性 2: GPU 减速

个性 3：断点持续运行

疾速开始

1. 差分进化算法

2. 遗传算法

2.2 遗传算法用于旅行商问题

3. 粒子群算法

3.1 带束缚的粒子群算法

3.2 不带束缚的粒子群算法

4. 模拟退火算法

4.1 模拟退火算法用于多元函数优化

4.2 模拟退火算法解决 TSP 问题（旅行商问题）

5. 蚁群算法

6. 免疫优化算法

7. 人工鱼群算法

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