-
Notifications
You must be signed in to change notification settings - Fork 41
/
Copy pathSCA.py
102 lines (89 loc) · 3.94 KB
/
SCA.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
# ！usr/bin/env python
# -*- coding: utf-8 -*-
# Time : 2021/12/8 14:20
# @Author : LucXiong
# @Project : Model
# @File : SCA_new.py
"""
Ref:https://github.com/luizaes/sca-algorithm
S. Mirjalili, SCA: A Sine Cosine Algorithm for Solving Optimization Problems, Knowledge-based Systems, in press, 2015, DOI: http://dx.doi.org/10.1016/j.knosys.2015.12.022
"""
import numpy as np
import random
import math
import matplotlib.pyplot as plt
import test_function
class sca():
def __init__(self, pop_size=5, n_dim=2, a=2, lb=-1e5, ub=1e5, max_iter=20, func=None):
self.pop = pop_size
self.n_dim = n_dim
self.a = a # 感知概率
self.func = func
self.max_iter = max_iter # max iter
self.lb, self.ub = np.array(lb) * np.ones(self.n_dim), np.array(ub) * np.ones(self.n_dim)
assert self.n_dim == len(self.lb) == len(self.ub), 'dim == len(lb) == len(ub) is not True'
assert np.all(self.ub > self.lb), 'upper-bound must be greater than lower-bound'
self.X = np.random.uniform(low=self.lb, high=self.ub, size=(self.pop, self.n_dim))
self.Y = [self.func(self.X[i]) for i in range(len(self.X))] # y = f(x) for all particles
self.pbest_x = self.X.copy() # personal best location of every particle in history
self.pbest_y = [np.inf for i in range(self.pop)] # best image of every particle in history
self.gbest_x = self.pbest_x.mean(axis=0).reshape(1, -1) # global best location for all particles
self.gbest_y = np.inf # global best y for all particles
self.gbest_y_hist = [] # gbest_y of every iteration
self.update_gbest()
def update_pbest(self):
'''
personal best
:return:
'''
for i in range(len(self.Y)):
if self.pbest_y[i] > self.Y[i]:
self.pbest_x[i] = self.X[i]
self.pbest_y[i] = self.Y[i]
def update_gbest(self):
'''
global best
:return:
'''
idx_min = self.pbest_y.index(min(self.pbest_y))
if self.gbest_y > self.pbest_y[idx_min]:
self.gbest_x = self.X[idx_min, :].copy()
self.gbest_y = self.pbest_y[idx_min]
def update(self, i):
r1 = self.a - i * ((self.a) / self.max_iter)
for j in range(self.pop):
for k in range(self.n_dim):
r2 = 2 * math.pi * random.uniform(0.0, 1.0)
r3 = 2 * random.uniform(0.0, 1.0)
r4 = random.uniform(0.0, 1.0)
if r4 < 0.5:
try:
self.X[j][k] = self.X[j][k] + (r1 * math.sin(r2) * abs(r3 * self.gbest_x[k] - self.X[j][k]))
except:
self.X[j][k] = self.X[j][k] + (r1 * math.sin(r2) * abs(r3 * self.gbest_x[0][k] - self.X[j][k]))
else:
try:
self.X[j][k] = self.X[j][k] + (r1 * math.cos(r2) * abs(r3 * self.gbest_x[k] - self.X[j][k]))
except:
self.X[j][k] = self.X[j][k] + (r1 * math.cos(r2) * abs(r3 * self.gbest_x[0][k] - self.X[j][k]))
self.X = np.clip(self.X, self.lb, self.ub)
self.Y = [self.func(self.X[i]) for i in range(len(self.X))] # Function for fitness evaluation of new solutions
def run(self):
for i in range(self.max_iter):
self.update(i)
self.update_pbest()
self.update_gbest()
self.gbest_y_hist.append(self.gbest_y)
self.best_x, self.best_y = self.gbest_x, self.gbest_y
return self.best_x, self.best_y
if __name__ == '__main__':
n_dim = 2
lb = [-512 for i in range(n_dim)]
ub = [512 for i in range(n_dim)]
demo_func = test_function.f23
sca = sca(n_dim=2, pop_size=40, max_iter=150, lb=lb, ub=ub, func=demo_func)
sca.run()
print('best_x is ', sca.gbest_x, 'best_y is', sca.gbest_y)
print(f'{demo_func(sca.gbest_x)}\t{sca.gbest_x}')
plt.plot(sca.gbest_y_hist)
plt.show()