Swarm Intelligence in Python
Project description
scikit-opt
Swarm Intelligence 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
For the current developer version:
git clone git@github.com:guofei9987/scikit-opt.git
cd scikit-opt
pip install .
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
Step1:define your problem
-> 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)
Step2: do GA
-> 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)
Step3: plot the result
-> 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()
1.1 Genetic Algorithm for TSP(Travelling Salesman Problem)
Just import the GA_TSP
, it overloads the crossover
, mutation
to solve the TSP
Step1: define your problem. Prepare your points coordinate and 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)])
Step2: 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()
Step3: 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
Step1: define your problem:
-> Demo code: examples/demo_pso.py#s1
def demo_func(x):
x1, x2, x3 = x
return x1 ** 2 + (x2 - 0.05) ** 2 + x3 ** 2
Step2: do PSO
-> 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)
Step3: Plot the result
-> Demo code: examples/demo_pso.py#s3
import matplotlib.pyplot as plt
plt.plot(pso.gbest_y_hist)
plt.show()
2.2 PSO without constraint
-> 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)
3. SA(Simulated Annealing)
3.1 SA for multiple function
Step1: define your problem
-> Demo code: examples/demo_sa.py#s1
demo_func = lambda x: x[0] ** 2 + (x[1] - 0.05) ** 2 + x[2] ** 2
Step2: do SA
-> 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)
Step3: Plot the result
-> 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()
Moreover, scikit-opt provide 3 types of Simulated Annealing: Fast, Boltzmann, Cauchy. See more sa
3.2 SA for TSP
Step1: oh, yes, define your problems. To boring to copy this step.
Step2: 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), 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))
Step3: plot the result
-> 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("Distance")
ax[0].set_ylabel("Iteration")
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()
More: Plot the animation:
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)
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