/
code.py
139 lines (124 loc) · 6.11 KB
/
code.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
import numpy as np
from pypop7.optimizers.de.de import DE
from pypop7.optimizers.de.cde import CDE
class CODE(CDE):
"""COmposite Differential Evolution (CODE).
Parameters
----------
problem : `dict`
problem arguments with the following common settings (`keys`):
* 'fitness_function' - objective function to be **minimized** (`func`),
* 'ndim_problem' - number of dimensionality (`int`),
* 'upper_boundary' - upper boundary of search range (`array_like`),
* 'lower_boundary' - lower boundary of search range (`array_like`).
options : `dict`
optimizer options with the following common settings (`keys`):
* 'max_function_evaluations' - maximum of function evaluations (`int`, default: `np.Inf`),
* 'max_runtime' - maximal runtime to be allowed (`float`, default: `np.Inf`),
* 'seed_rng' - seed for random number generation needed to be *explicitly* set (`int`);
and with the following particular setting (`key`):
* 'n_individuals' - population size (`int`, default: `100`).
Examples
--------
Use the optimizer to minimize the well-known test function
`Rosenbrock <http://en.wikipedia.org/wiki/Rosenbrock_function>`_:
.. code-block:: python
:linenos:
>>> import numpy
>>> from pypop7.benchmarks.base_functions import rosenbrock # function to be minimized
>>> from pypop7.optimizers.de.code import CODE
>>> problem = {'fitness_function': rosenbrock, # define problem arguments
... 'ndim_problem': 2,
... 'lower_boundary': -5*numpy.ones((2,)),
... 'upper_boundary': 5*numpy.ones((2,))}
>>> options = {'max_function_evaluations': 5000, # set optimizer options
... 'seed_rng': 0}
>>> code = CODE(problem, options) # initialize the optimizer class
>>> results = code.optimize() # run the optimization process
>>> # return the number of function evaluations and best-so-far fitness
>>> print(f"CODE: {results['n_function_evaluations']}, {results['best_so_far_y']}")
CODE: 5000, 0.01052980838183792
Attributes
----------
n_individuals : `int`
number of offspring, aka offspring population size.
References
----------
Wang, Y., Cai, Z., and Zhang, Q. 2011.
Differential evolution with composite trial vector generation strategies and control parameters.
IEEE Transactions on Evolutionary Computation, 15(1), pp.55–66.
https://doi.org/10.1109/TEVC.2010.2087271
"""
def __init__(self, problem, options):
CDE.__init__(self, problem, options)
self.is_bound = options.get('is_bound', False)
self._pool = [[1.0, 0.1], [1.0, 0.9], [0.8, 0.2]] # a pool of two control parameters (f, cr)
def bound(self, x=None):
if not self.is_bound:
return x
for k in range(self.n_individuals):
idx = np.array(x[k] < self.lower_boundary)
if idx.any():
x[k][idx] = np.minimum(self.upper_boundary, 2.0*self.lower_boundary - x[k])[idx]
idx = np.array(x[k] > self.upper_boundary)
if idx.any():
x[k][idx] = np.maximum(self.lower_boundary, 2.0*self.upper_boundary - x[k])[idx]
return x
def mutate(self, x=None):
x1 = np.empty((self.n_individuals, self.ndim_problem))
x2 = np.empty((self.n_individuals, self.ndim_problem))
x3 = np.empty((self.n_individuals, self.ndim_problem))
# randomly select from the parameter candidate pool
base = np.arange(self.n_individuals)
f_p = self.rng_optimization.choice(self._pool, (self.n_individuals, 3))
for k in range(self.n_individuals):
base_k = [i for i in base if i != k]
r = self.rng_optimization.choice(base_k, (3,), False)
x1[k] = x[r[0]] + f_p[k, 0, 0]*(x[r[1]] - x[r[2]]) # rand/1/bin
r = self.rng_optimization.choice(base_k, (5,), False)
x2[k] = (x[r[0]] + self.rng_optimization.random()*(x[r[1]] - x[r[2]]) +
f_p[k, 1, 0]*(x[r[3]] - x[r[4]])) # rand/2/bin
r = self.rng_optimization.choice(base_k, (3,), False)
x3[k] = (x[k] + self.rng_optimization.random()*(x[r[0]] - x[k]) +
f_p[k, 2, 0]*(x[r[1]] - x[r[2]])) # current-to-rand/1
return x1, x2, x3, f_p
def crossover(self, x_mu=None, x=None, p_cr=None):
x_cr = np.copy(x)
for k in range(self.n_individuals):
j_r = self.rng_optimization.integers(self.ndim_problem)
for i in range(self.ndim_problem):
if (i == j_r) or (self.rng_optimization.random() < p_cr[k]):
x_cr[k, i] = x_mu[k, i]
return x_cr
def select(self, x=None, y=None, x_cr=None, args=None):
yyy = [] # to store all fitnesses
for k in range(self.n_individuals):
if self._check_terminations():
break
yy = self._evaluate_fitness(x_cr[k], args)
yyy.append(yy)
if yy < y[k]:
x[k], y[k] = x_cr[k], yy
return x, y, yyy
def iterate(self, x=None, y=None, args=None):
yy = [] # to store all fitnesses
x1, x2, x3, f_p = self.mutate(x)
x1 = self.bound(self.crossover(x1, x, f_p[:, 0, 1]))
x2 = self.bound(self.crossover(x2, x, f_p[:, 1, 1]))
x3 = self.bound(x3)
x, y, yy_1 = self.select(x, y, x1, args)
x, y, yy_2 = self.select(x, y, x2, args)
x, y, yy_3 = self.select(x, y, x3, args)
yy.extend(yy_1)
yy.extend(yy_2)
yy.extend(yy_3)
self._n_generations += 1
return x, y, yy
def optimize(self, fitness_function=None, args=None):
fitness = DE.optimize(self, fitness_function)
x, y, _ = self.initialize(args)
yy = y
while not self._check_terminations():
self._print_verbose_info(fitness, yy)
x, y, yy = self.iterate(x, y, args)
return self._collect(fitness, yy)