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-- PSO
import pyswarms as ps
from pyswarms.single.global_best import GlobalBestPSO
import matplotlib.pyplot as plt
from pyswarms.utils.plotters import plot_cost_history
from pyswarms.utils.plotters.formatters import Mesher, Designer
import numpy as np
import math
options = {'c1': 0.5, 'c2': 0.3, 'w': 0.9}
bounds = (x_min, x_max)
optimizer = GlobalBestPSO(n_particles=100, dimensions=2, options=options,bounds=bounds)
cost, pos = optimizer.optimize(func, iters=200)
-- GA

from genetic_algorithm.main import genetic_optimisation
param_space = {"x1": [-5, 5],"x2": [-5, 5]}
xopt=genetic_optimisation(input_model=fitness, param_space=param_space, pop_size=100,
num_parents=2,max_num_generations=500, mutation_prob=0.5, stoping_rounds=50, integer_params=[])
print(xopt.get('best fitness'))
print(xopt.get('best params'))

--draw
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
x1v = np.arange(-5, 5, 0.01)
x2v = np.arange(-5, 5, 0.01)
x1, x2 = np.meshgrid(x1v, x2v)
f = np.sin(x1) + np.cos(x2)
fig = plt.figure()
ax = fig.gca(projection=’3d’)
p1 = ax.plot_surface(x1, x2, f)
plt.show()