1
|
|
|
# Author: Simon Blanke |
2
|
|
|
# Email: [email protected] |
3
|
|
|
# License: MIT License |
4
|
|
|
|
5
|
|
|
|
6
|
|
|
import random |
7
|
|
|
|
8
|
|
|
import numpy as np |
9
|
|
|
|
10
|
|
|
from ...base_optimizer import BaseOptimizer |
11
|
|
|
from ...base_positioner import BasePositioner |
12
|
|
|
|
13
|
|
|
|
14
|
|
|
class ParticleSwarmOptimizer(BaseOptimizer): |
15
|
|
|
def __init__(self, _main_args_, _opt_args_): |
16
|
|
|
super().__init__(_main_args_, _opt_args_) |
17
|
|
|
|
18
|
|
|
def _init_particles(self, _cand_): |
19
|
|
|
_p_list_ = [Particle() for _ in range(self._opt_args_.n_particles)] |
20
|
|
|
for i, _p_ in enumerate(_p_list_): |
21
|
|
|
_p_.nr = i |
22
|
|
|
_p_.pos_current = _cand_._space_.get_random_pos() |
23
|
|
|
_p_.pos_best = _p_.pos_current |
24
|
|
|
_p_.velo = np.zeros(len(_cand_._space_.search_space)) |
25
|
|
|
|
26
|
|
|
return _p_list_ |
27
|
|
|
|
28
|
|
|
def _move_positioners(self, _cand_, _p_list_): |
29
|
|
|
for _p_ in _p_list_: |
30
|
|
|
r1, r2 = random.random(), random.random() |
31
|
|
|
|
32
|
|
|
A = self._opt_args_.inertia * _p_.velo |
33
|
|
|
B = ( |
34
|
|
|
self._opt_args_.cognitive_weight |
35
|
|
|
* r1 |
36
|
|
|
* np.subtract(_p_.pos_best, _p_.pos_current) |
37
|
|
|
) |
38
|
|
|
C = ( |
39
|
|
|
self._opt_args_.social_weight |
40
|
|
|
* r2 |
41
|
|
|
* np.subtract(_cand_.pos_best, _p_.pos_current) |
42
|
|
|
) |
43
|
|
|
|
44
|
|
|
new_velocity = A + B + C |
45
|
|
|
|
46
|
|
|
_p_.velo = new_velocity |
47
|
|
|
_p_.pos_new = _p_.move_part(_cand_, _p_.pos_current) |
48
|
|
|
|
49
|
|
|
def _eval_particle(self, _cand_, _p_): |
50
|
|
|
_p_.score_new = _cand_.eval_pos(_p_.pos_new) |
51
|
|
|
|
52
|
|
|
if _p_.score_new > _cand_.score_best: |
53
|
|
|
_cand_, _p_ = self._update_pos(_cand_, _p_) |
54
|
|
|
|
55
|
|
|
def _iterate(self, i, _cand_, _p_list_): |
56
|
|
|
if i % self._opt_args_.n_particles == 0: |
57
|
|
|
self._move_positioners(_cand_, _p_list_) |
58
|
|
|
|
59
|
|
|
_p_current = _p_list_[i % self._opt_args_.n_particles] |
60
|
|
|
self._eval_particle(_cand_, _p_current) |
61
|
|
|
|
62
|
|
|
return _cand_ |
63
|
|
|
|
64
|
|
|
def _init_opt_positioner(self, _cand_): |
65
|
|
|
_p_list_ = self._init_particles(_cand_) |
66
|
|
|
|
67
|
|
|
for _p_ in _p_list_: |
68
|
|
|
_p_.score_current = _cand_.eval_pos(_p_.pos_current) |
69
|
|
|
_p_.score_best = _p_.score_current |
70
|
|
|
|
71
|
|
|
return _p_list_ |
72
|
|
|
|
73
|
|
|
|
74
|
|
|
class Particle(BasePositioner): |
75
|
|
|
def __init__(self): |
76
|
|
|
super().__init__(self) |
77
|
|
|
self.nr = None |
78
|
|
|
self.velo = None |
79
|
|
|
|
80
|
|
|
def move_part(self, _cand_, pos): |
81
|
|
|
pos_new = (pos + self.velo).astype(int) |
82
|
|
|
# limit movement |
83
|
|
|
n_zeros = [0] * len(_cand_._space_.dim) |
84
|
|
|
return np.clip(pos_new, n_zeros, _cand_._space_.dim) |
85
|
|
|
|