用 Python 進行差分進化全局優(yōu)化
差分進化優(yōu)化是一種進化算法，旨在與實值候選解一起使用。 如何在python中使用差異進化優(yōu)化算法API。 使用差異演化解決具有多個最優(yōu)解的全局優(yōu)化問題的示例。
差異進化 差分進化API 差分進化的工作實例
DE / x / y / zDE / best / 1 / bin和DE / best / 2 / bin是受歡迎的配置，因為它們在許多目標功能上表現(xiàn)良好。既然我們已經(jīng)熟悉了差分進化算法，那么讓我們看一下如何使用SciPy API實現(xiàn)。differential_evolution（）SciPy函數(shù)使用差分演化全局優(yōu)化算法。該函數(shù)將目標函數(shù)的名稱和每個輸入變量的界限作為搜索的最小參數(shù)。# perform the differential evolution search
result = differential_evolution(objective, bounds)
“ best1bin”（DE / best / 1 / bin），對于大多數(shù)問題而言，這是一個很好的配置。它通過從總體中選擇隨機解，從另一個中減去一個，然后將差異的縮放版本添加到總體中的最佳候選解中，來創(chuàng)建新的候選解。new = best + (mutation * (rand1 – r and2))
# ackley multimodal function
from numpy import arange
from numpy import exp
from numpy import sqrt
from numpy import cos
from numpy import e
from numpy import pi
from numpy import meshgrid
from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D
# objective function
def objective(x, y):
return -20.0 * exp(-0.2 * sqrt(0.5 * (x**2 + y**2))) - exp(0.5 * (cos(2 * pi * x) + cos(2 * pi * y))) + e + 20
# define range for input
r_min, r_max = -5.0, 5.0
# sample input range uniformly at 0.1 increments
xaxis = arange(r_min, r_max, 0.1)
yaxis = arange(r_min, r_max, 0.1)
# create a mesh from the axis
x, y = meshgrid(xaxis, yaxis)
# compute targets
results = objective(x, y)
# create a surface plot with the jet color scheme
figure = pyplot.figure()
axis = figure.gca(projection='3d')
axis.plot_surface(x, y, results, cmap='jet')
# show the plot
pyplot.show()
# define the bounds on the search
bounds = [[r_min, r_max], [r_min, r_max]]
# perform the differential evolution search
result = differential_evolution(objective, bounds)
# summarize the result
print('Status : %s' % result['message'])
print('Total Evaluations: %d' % result['nfev'])
# evaluate solution
solution = result['x']
evaluation = objective(solution)
print('Solution: f(%s) = %.5f' % (solution, evaluation))
# differential evolution global optimization for the ackley multimodal objective function
from scipy.optimize import differential_evolution
from numpy.random import rand
from numpy import exp
from numpy import sqrt
from numpy import cos
from numpy import e
from numpy import pi
# objective function
def objective(v):
x, y = v
return -20.0 * exp(-0.2 * sqrt(0.5 * (x**2 + y**2))) - exp(0.5 * (cos(2 * pi * x) + cos(2 * pi * y))) + e + 20
# define range for input
r_min, r_max = -5.0, 5.0
# define the bounds on the search
bounds = [[r_min, r_max], [r_min, r_max]]
# perform the differential evolution search
result = differential_evolution(objective, bounds)
# summarize the result
print('Status : %s' % result['message'])
print('Total Evaluations: %d' % result['nfev'])
# evaluate solution
solution = result['x']
evaluation = objective(solution)
print('Solution: f(%s) = %.5f' % (solution, evaluation))
Status: Optimization terminated successfully.
Total Evaluations: 3063
Solution: f([0. 0.]) = 0.00000
作者：沂水寒城，CSDN博客專家，個人研究方向：機器學(xué)習(xí)、深度學(xué)習(xí)、NLP、CV
Blog: http://yishuihancheng.blog.csdn.net
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