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-opt

For the current developer version:

git clone git@github.com: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, ranking of 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
    https://en.wikipedia.org/wiki/Test_functions_for_optimization
    '''
    x1, x2 = p
    part1 = np.square(x1) - np.square(x2)
    part2 = np.square(x1) + np.square(x2)
    return 0.5 + (np.square(np.sin(part1)) - 0.5) / np.square(1 + 0.001 * part2)

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] ** 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

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:

↑see examples/demo_sa_tsp.py

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.

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README.md

README.md

scikit-opt

install

Features

Feature1: UDF

feature2: continue to run

feature3: 4-ways to accelerate

feature4: GPU computation

Quick start

1. Differential Evolution

2. Genetic Algorithm

2.2 Genetic Algorithm for TSP(Travelling Salesman Problem)

3. PSO(Particle swarm optimization)

3.1 PSO

3.2 PSO with nonlinear constraint

4. SA(Simulated Annealing)

4.1 SA for multiple function

4.2 SA for TSP

5. ACA (Ant Colony Algorithm) for tsp

6. immune algorithm (IA)

7. Artificial Fish Swarm Algorithm (AFSA)

Projects using scikit-opt

Files

README.md

Latest commit

History

README.md

File metadata and controls

scikit-opt

install

Features

Feature1: UDF

feature2: continue to run

feature3: 4-ways to accelerate

feature4: GPU computation

Quick start

1. Differential Evolution

2. Genetic Algorithm

2.2 Genetic Algorithm for TSP(Travelling Salesman Problem)

3. PSO(Particle swarm optimization)

3.1 PSO

3.2 PSO with nonlinear constraint

4. SA(Simulated Annealing)

4.1 SA for multiple function

4.2 SA for TSP

5. ACA (Ant Colony Algorithm) for tsp

6. immune algorithm (IA)

7. Artificial Fish Swarm Algorithm (AFSA)

Projects using scikit-opt