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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
pip install scikit-opt
For the current developer version:
git clone git@github.com:guofei9987/scikit-opt.git
cd scikit-opt
pip install .
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)
(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)
- vectorization
- multithreading
- multiprocessing
- cached
see https://github.com/guofei9987/scikit-opt/blob/master/examples/example_function_modes.py
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
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)
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()
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()
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()
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:
pso_ani
↑see examples/demo_pso_ani.py
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
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:
sa
↑see examples/demo_sa_tsp.py
-> 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()
-> 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)
-> 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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