python遗传算法多目标规划（易部署的Python遗传算法库）
python遗传算法多目标规划（易部署的Python遗传算法库）best_x best_y = ga.run() print('best_x:' best_x '\n' 'best_y:' best_y) 现在 udf 支持遗传算法的这几个算子： crossover mutation selection rankingfrom sko.operators import ranking selection crossover mutation ga.register(operator_name='ranking' operator=ranking.ranking). \ register(operator_name='crossover' operator=crossover.crossover_2point). \ registe
scikit-opt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
或者直接把源代码中的 sko 文件夹下载下来放本地也调用可以
特性特性1：UDF（用户自定义算子）
举例来说，你想出一种新的“选择算子”，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，并且创建遗传算法实例
-> 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建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)
现在 udf 支持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
提供一个面向对象风格的自定义算子的方法，供进阶用户使用:
-> 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)
特性2: GPU 加速
GPU加速功能还比较简单，将会在 1.0.0 版本大大完善。
有个 demo 已经可以在现版本运行了
特性3：断点继续运行
例如，先跑10代，然后在此基础上再跑20代，可以这么写：
from sko.GA import GA
func = lambda x: x[0] ** 2
ga = GA(func=func n_dim=1)
ga.run(10)
ga.run(20)快速开始
1. 差分进化算法
Step1：定义你的问题，这个demo定义了有约束优化问题
-> 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)
2. 遗传算法
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 画出结果
-> 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()
2.2 遗传算法用于旅行商问题
GA_TSP 针对TSP问题重载了 交叉(crossover)、变异(mutation) 两个算子
第一步，定义问题。
这里作为demo，随机生成距离矩阵. 实战中从真实数据源中读取。
-> 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，画出结果：
-> 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()
3. 粒子群算法
(PSO Particle swarm optimization)
3.1 带约束的粒子群算法
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，画出结果
-> 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
3.2 不带约束的粒子群算法
-> 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)
4. 模拟退火算法
(SA Simulated Annealing)
4.1 模拟退火算法用于多元函数优化
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，画出结果
-> 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
4.2 模拟退火算法解决TSP问题（旅行商问题）
第一步，定义问题。（我猜你已经无聊了，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，画出结果
-> 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
5. 蚁群算法
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()
6. 免疫优化算法
(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)
7. 人工鱼群算法
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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