sinketsuzao/scikit-opt

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm (Heuristic Algorithms) and TSP in Python（遗传算法、粒子群算法、模拟退火算法、蚁群算法、免疫优化算法、鱼群算法）

scikit-opt

Heuristic Algorithms in Python
(Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,Artificial Fish Swarm Algorithm in Python)

• Documentation: https://scikit-opt.github.io/scikit-opt/#/en/
• 文档： https://scikit-opt.github.io/scikit-opt/#/zh/
• Source code: https://github.com/guofei9987/scikit-opt

install

pip install scikit-opt

News:

All algorithms will be available on TensorFlow/Spark pytorch on version 0.4, getting parallel performance.
DE(Differential Evolution Algorithm) will be complete on version 0.5
Have fun!

feature: UDF

UDF (user defined function) is available now!

For example, you just worked out a new type of selection function.
Now, your selection function is like this:
-> Demo code: examples/demo_ga_udf.py#s1

# step1: define your own operator:
def selection_tournament(self, tourn_size):
    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

Import and build ga
-> 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] ** 2
ga = GA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, lb=[-1, -10, -5], ub=[2, 10, 2])

Regist your udf to GA
-> Demo code: examples/demo_ga_udf.py#s3

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

scikit-opt also provide some operators
-> Demo code: examples/demo_ga_udf.py#s4

from sko.GA import ranking_linear, ranking_raw, crossover_2point, selection_roulette_2, mutation

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

Now do GA as usual
-> Demo code: examples/demo_ga_udf.py#s5

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

Until Now, the udf surport crossover, mutation, selection, ranking of GA

scikit-opt provide a dozen of operators, see here

Quick start

1. Genetic Algorithm

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

import numpy as np
from sko.GA import GA


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)


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)

plot the result using matplotlib
-> Demo code: examples/demo_ga.py#s2

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()

1.1 Genetic Algorithm for TSP(Travelling Salesman Problem)

Just import the GA_TSP, it overloads the crossover, mutation to solve the TSP

Firstly, prepare your data (the distance matrix). Here I generate the data randomly as a demo:
-> Demo code: examples/demo_ga_tsp.py#s1

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

num_points = 8

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)])

Do GA
-> 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=300, max_iter=800, Pm=0.3)
best_points, best_distance = ga_tsp.run()

Plot the result:
-> Demo code: examples/demo_ga_tsp.py#s3

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

2. PSO(Particle swarm optimization)

2.1 PSO with constraint

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

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


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])
pso.run()
print('best_x is ', pso.gbest_x, 'best_y is', pso.gbest_y)

import matplotlib.pyplot as plt

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

↑see examples/demo_pso.py

2.2 PSO without constraint

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

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

3. SA(Simulated Annealing)

3.1 SA for multiple function

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

from sko.SA import SA

demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2
sa = SA(func=demo_func, x0=[1, 1, 1])
x_star, y_star = sa.run()
print(x_star, y_star)

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

import matplotlib.pyplot as plt
import pandas as pd

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

3.2 SA for TSP

Firstly, prepare your data (the distance matrix). See GA_TSP.

DO SA for TSP
-> 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))

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

plot the result
-> 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))

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

3.2 SA for real function optimization

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

from sko.SA import SA

demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2
sa = SA(func=demo_func, x0=[1, 1, 1])
x_star, y_star = sa.run()
print(x_star, y_star)

4. ACA (Ant Colony Algorithm) for tsp

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

from sko.ACA import ACA_TSP

aca = ACA_TSP(func=cal_total_distance, n_dim=8,
              size_pop=10, max_iter=20,
              distance_matrix=distance_matrix)

best_x, best_y = aca.run()

5. 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, pop=500, max_iter=2000, Pm=0.2,
                T=0.7, alpha=0.95)
best_points, best_distance = ia_tsp.run()
print('best routine:', best_points, 'best_distance:', best_distance)

6. artificial fish swarm algorithm (AFSA)

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

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


from sko.ASFA import ASFA

asfa = ASFA(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 = asfa.fit()
print(best_x, best_y)