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
- Help us improve scikit-opt https://www.wjx.cn/jq/50964691.aspx
install
pip install scikit-optFor the current developer version:
git clone [email protected]:guofei9987/scikit-opt.git
cd scikit-opt
pip install .
Features
Feature1: 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(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
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] - 0.5) ** 2
ga = GA(func=demo_func, n_dim=3, size_pop=100, max_iter=500, prob_mut=0.001,
lb=[-1, -10, -5], ub=[2, 10, 2], precision=[1e-7, 1e-7, 1])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.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)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,rankingof GA scikit-opt provide a dozen of operators, see here
For advanced users:
-> 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)feature2: continue to run
(New in version 0.3.6)
Run an algorithm for 10 iterations, and then run another 20 iterations base on the 10 iterations before:
from sko.GA import GA
func = lambda x: x[0] ** 2
ga = GA(func=func, n_dim=1)
ga.run(10)
ga.run(20)feature3: 4-ways to accelerate
- vectorization
- multithreading
- multiprocessing
- cached
see https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py
feature4: GPU computation
We are developing GPU computation, which will be stable on version 1.0.0
An example is already available: https://github.com/guofei9987/scikit-opt/blob/master/examples/demo_ga_gpu.py
Quick start
1. Differential Evolution
Step1：define your problem
-> 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: do Differential Evolution
-> 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. 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.square(np.sin(x)) - 0.5) / np.square(1 + 0.001 * x)
Step2: do Genetic Algorithm
-> 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, prob_mut=0.001, 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)-> 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 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 = 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)])
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=50, max_iter=500, prob_mut=1)
best_points, best_distance = ga_tsp.run()Step3: Plot the result:
-> 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. PSO(Particle swarm optimization)
3.1 PSO
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, n_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()
3.2 PSO with nonlinear constraint
If you need nolinear constraint like (x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2<=0
Codes are like this:
constraint_ueq = (
lambda x: (x[0] - 1) ** 2 + (x[1] - 0) ** 2 - 0.5 ** 2
,
)
pso = PSO(func=demo_func, n_dim=2, pop=40, max_iter=max_iter, lb=[-2, -2], ub=[2, 2]
, constraint_ueq=constraint_ueq)Note that, you can add more then one nonlinear constraint. Just add it to constraint_ueq
More over, we have an animation:
↑see examples/demo_pso_ani.py
4. SA(Simulated Annealing)
4.1 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] ** 2Step2: 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
4.2 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("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()
More: Plot the animation:
5. 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=num_points,
size_pop=50, max_iter=200,
distance_matrix=distance_matrix)
best_x, best_y = aca.run()
6. 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. 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)Projects using scikit-opt
- Yu, J., He, Y., Yan, Q., & Kang, X. (2021). SpecView: Malware Spectrum Visualization Framework With Singular Spectrum Transformation. IEEE Transactions on Information Forensics and Security, 16, 5093-5107.
- Zhen, H., Zhai, H., Ma, W., Zhao, L., Weng, Y., Xu, Y., ... & He, X. (2021). Design and tests of reinforcement-learning-based optimal power flow solution generator. Energy Reports.
- Heinrich, K., Zschech, P., Janiesch, C., & Bonin, M. (2021). Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decision Support Systems, 143, 113494.
- Tang, H. K., & Goh, S. K. (2021). A Novel Non-population-based Meta-heuristic Optimizer Inspired by the Philosophy of Yi Jing. arXiv preprint arXiv:2104.08564.
- Wu, G., Li, L., Li, X., Chen, Y., Chen, Z., Qiao, B., ... & Xia, L. (2021). Graph embedding based real-time social event matching for EBSNs recommendation. World Wide Web, 1-22.
- Pan, X., Zhang, Z., Zhang, H., Wen, Z., Ye, W., Yang, Y., ... & Zhao, X. (2021). A fast and robust mixture gases identification and concentration detection algorithm based on attention mechanism equipped recurrent neural network with double loss function. Sensors and Actuators B: Chemical, 342, 129982.
- Castella Balcell, M. (2021). Optimization of the station keeping system for the WindCrete floating offshore wind turbine.
- Zhai, B., Wang, Y., Wang, W., & Wu, B. (2021). Optimal Variable Speed Limit Control Strategy on Freeway Segments under Fog Conditions. arXiv preprint arXiv:2107.14406.
- Yap, X. H. (2021). Multi-label classification on locally-linear data: Application to chemical toxicity prediction.
- Gebhard, L. (2021). Expansion Planning of Low-Voltage Grids Using Ant Colony Optimization Ausbauplanung von Niederspannungsnetzen mithilfe eines Ameisenalgorithmus.
- Ma, X., Zhou, H., & Li, Z. (2021). Optimal Design for Interdependencies between Hydrogen and Power Systems. IEEE Transactions on Industry Applications.
- de Curso, T. D. C. (2021). Estudo do modelo Johansen-Ledoit-Sornette de bolhas financeiras.
- Wu, T., Liu, J., Liu, J., Huang, Z., Wu, H., Zhang, C., ... & Zhang, G. (2021). A Novel AI-based Framework for AoI-optimal Trajectory Planning in UAV-assisted Wireless Sensor Networks. IEEE Transactions on Wireless Communications.
- Liu, H., Wen, Z., & Cai, W. (2021, August). FastPSO: Towards Efficient Swarm Intelligence Algorithm on GPUs. In 50th International Conference on Parallel Processing (pp. 1-10).
- Mahbub, R. (2020). Algorithms and Optimization Techniques for Solving TSP.
- Li, J., Chen, T., Lim, K., Chen, L., Khan, S. A., Xie, J., & Wang, X. (2019). Deep learning accelerated gold nanocluster synthesis. Advanced Intelligent Systems, 1(3), 1900029.