用 Python 进行差分进化全局优化
差分进化优化是一种进化算法，旨在与实值候选解一起使用。 如何在python中使用差异进化优化算法API。 使用差异演化解决具有多个最优解的全局优化问题的示例。
差异进化 差分进化API 差分进化的工作实例
DE / x / y / zDE / best / 1 / bin和DE / best / 2 / bin是受欢迎的配置，因为它们在许多目标功能上表现良好。既然我们已经熟悉了差分进化算法，那么让我们看一下如何使用SciPy API实现。differential_evolution（）SciPy函数使用差分演化全局优化算法。该函数将目标函数的名称和每个输入变量的界限作为搜索的最小参数。# perform the differential evolution search
result = differential_evolution(objective, bounds)
“ best1bin”（DE / best / 1 / bin），对于大多数问题而言，这是一个很好的配置。它通过从总体中选择随机解，从另一个中减去一个，然后将差异的缩放版本添加到总体中的最佳候选解中，来创建新的候选解。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博客专家，个人研究方向：机器学习、深度学习、NLP、CV
Blog: http://yishuihancheng.blog.csdn.net
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