|
1
|
|
|
# Author: Simon Blanke |
|
2
|
|
|
# Email: [email protected] |
|
3
|
|
|
# License: MIT License |
|
4
|
|
|
|
|
5
|
|
|
import numpy as np |
|
6
|
|
|
|
|
7
|
|
|
from .base_population_optimizer import BasePopulationOptimizer |
|
8
|
|
|
from ._spiral import Spiral |
|
9
|
|
|
|
|
10
|
|
|
|
|
11
|
|
View Code Duplication |
def centeroid(array_list): |
|
|
|
|
|
|
12
|
|
|
centeroid = [] |
|
13
|
|
|
for idx in range(array_list[0].shape[0]): |
|
14
|
|
|
center_dim_pos = [] |
|
15
|
|
|
for array in array_list: |
|
16
|
|
|
center_dim_pos.append(array[idx]) |
|
17
|
|
|
|
|
18
|
|
|
center_dim_mean = np.array(center_dim_pos).mean() |
|
19
|
|
|
centeroid.append(center_dim_mean) |
|
20
|
|
|
|
|
21
|
|
|
return centeroid |
|
22
|
|
|
|
|
23
|
|
|
|
|
24
|
|
|
class SpiralOptimization(BasePopulationOptimizer): |
|
25
|
|
|
name = "Spiral Optimization" |
|
26
|
|
|
_name_ = "spiral_optimization" |
|
27
|
|
|
__name__ = "SpiralOptimization" |
|
28
|
|
|
|
|
29
|
|
|
optimizer_type = "population" |
|
30
|
|
|
computationally_expensive = False |
|
31
|
|
|
|
|
32
|
|
|
def __init__( |
|
33
|
|
|
self, |
|
34
|
|
|
search_space, |
|
35
|
|
|
initialize={"grid": 4, "random": 2, "vertices": 4}, |
|
36
|
|
|
constraints=[], |
|
37
|
|
|
random_state=None, |
|
38
|
|
|
rand_rest_p=0, |
|
39
|
|
|
nth_process=None, |
|
40
|
|
|
population=10, |
|
41
|
|
|
decay_rate=0.99, |
|
42
|
|
|
): |
|
43
|
|
|
super().__init__( |
|
44
|
|
|
search_space=search_space, |
|
45
|
|
|
initialize=initialize, |
|
46
|
|
|
constraints=constraints, |
|
47
|
|
|
random_state=random_state, |
|
48
|
|
|
rand_rest_p=rand_rest_p, |
|
49
|
|
|
nth_process=nth_process, |
|
50
|
|
|
) |
|
51
|
|
|
|
|
52
|
|
|
self.population = population |
|
53
|
|
|
self.decay_rate = decay_rate |
|
54
|
|
|
|
|
55
|
|
|
self.particles = self._create_population(Spiral) |
|
56
|
|
|
self.optimizers = self.particles |
|
57
|
|
|
|
|
58
|
|
|
@BasePopulationOptimizer.track_new_pos |
|
59
|
|
|
def init_pos(self): |
|
60
|
|
|
nth_pop = self.nth_trial % len(self.particles) |
|
61
|
|
|
|
|
62
|
|
|
self.p_current = self.particles[nth_pop] |
|
63
|
|
|
self.p_current.decay_rate = self.decay_rate |
|
64
|
|
|
|
|
65
|
|
|
return self.p_current.init_pos() |
|
66
|
|
|
|
|
67
|
|
|
def finish_initialization(self): |
|
68
|
|
|
self.sort_pop_best_score() |
|
69
|
|
|
self.center_pos = self.pop_sorted[0].pos_current |
|
70
|
|
|
self.center_score = self.pop_sorted[0].score_current |
|
71
|
|
|
|
|
72
|
|
|
self.search_state = "iter" |
|
73
|
|
|
|
|
74
|
|
|
@BasePopulationOptimizer.track_new_pos |
|
75
|
|
|
def iterate(self): |
|
76
|
|
|
while True: |
|
77
|
|
|
self.p_current = self.particles[ |
|
78
|
|
|
self.nth_trial % len(self.particles) |
|
79
|
|
|
] |
|
80
|
|
|
|
|
81
|
|
|
self.sort_pop_best_score() |
|
82
|
|
|
self.p_current.global_pos_best = self.pop_sorted[0].pos_current |
|
83
|
|
|
|
|
84
|
|
|
pos_new = self.p_current.move_spiral(self.center_pos) |
|
85
|
|
|
|
|
86
|
|
|
if self.conv.not_in_constraint(pos_new): |
|
87
|
|
|
return pos_new |
|
88
|
|
|
return self.p_current.iterate() |
|
89
|
|
|
|
|
90
|
|
|
@BasePopulationOptimizer.track_new_score |
|
91
|
|
|
def evaluate(self, score_new): |
|
92
|
|
|
if self.search_state == "iter": |
|
93
|
|
|
if self.pop_sorted[0].score_current > self.center_score: |
|
94
|
|
|
self.center_pos = self.pop_sorted[0].pos_current |
|
95
|
|
|
self.center_score = self.pop_sorted[0].score_current |
|
96
|
|
|
|
|
97
|
|
|
self.p_current.evaluate(score_new) |
|
98
|
|
|
|
SimonBlanke /
Gradient-Free-Optimizers
Loading history...