|
1
|
|
|
# encoding=utf8 |
|
2
|
|
|
# This is temporary fix to import module from parent folder |
|
3
|
|
|
# It will be removed when package is published on PyPI |
|
4
|
|
|
import sys |
|
5
|
|
|
sys.path.append('../') |
|
6
|
|
|
# End of fix |
|
7
|
|
|
|
|
8
|
|
|
import random |
|
9
|
|
|
import logging |
|
10
|
|
|
from NiaPy.algorithms.basic import GlowwormSwarmOptimizationV1 |
|
11
|
|
|
from NiaPy.util import Task, TaskConvPrint, TaskConvPlot, OptimizationType, getDictArgs |
|
12
|
|
|
|
|
13
|
|
|
logging.basicConfig() |
|
14
|
|
|
logger = logging.getLogger('examples') |
|
15
|
|
|
logger.setLevel('INFO') |
|
16
|
|
|
|
|
17
|
|
|
# For reproducive results |
|
18
|
|
|
random.seed(1234) |
|
19
|
|
|
|
|
20
|
|
View Code Duplication |
class MinMB(object): |
|
|
|
|
|
|
21
|
|
|
def __init__(self): |
|
22
|
|
|
self.Lower = -11 |
|
23
|
|
|
self.Upper = 11 |
|
24
|
|
|
|
|
25
|
|
|
def function(self): |
|
26
|
|
|
def evaluate(D, sol): |
|
27
|
|
|
val = 0.0 |
|
28
|
|
|
for i in range(D): val = val + sol[i] * sol[i] |
|
29
|
|
|
return val |
|
30
|
|
|
return evaluate |
|
31
|
|
|
|
|
32
|
|
|
class MaxMB(MinMB): |
|
33
|
|
|
def function(self): |
|
34
|
|
|
f = MinMB.function(self) |
|
35
|
|
|
def e(D, sol): return -f(D, sol) |
|
36
|
|
|
return e |
|
37
|
|
|
|
|
38
|
|
|
def simple_example(alg, runs=10, D=10, nFES=50000, nGEN=10000, seed=[None], optType=OptimizationType.MINIMIZATION, optFunc=MinMB, **kn): |
|
39
|
|
|
for i in range(runs): |
|
40
|
|
|
task = Task(D=D, nFES=nFES, nGEN=nGEN, optType=optType, benchmark=optFunc()) |
|
41
|
|
|
algo = alg(seed=seed[i % len(seed)], task=task) |
|
42
|
|
|
best = algo.run() |
|
43
|
|
|
logger.info('%s %s' % (best[0], best[1])) |
|
44
|
|
|
|
|
45
|
|
|
def logging_example(alg, D=10, nFES=50000, nGEN=100000, seed=[None], optType=OptimizationType.MINIMIZATION, optFunc=MinMB, **kn): |
|
46
|
|
|
task = TaskConvPrint(D=D, nFES=nFES, nGEN=nGEN, optType=optType, benchmark=optFunc()) |
|
47
|
|
|
algo = alg(seed=seed[0], task=task) |
|
48
|
|
|
best = algo.run() |
|
49
|
|
|
logger.info('%s %s' % (best[0], best[1])) |
|
50
|
|
|
|
|
51
|
|
|
def plot_example(alg, D=10, nFES=50000, nGEN=100000, seed=[None], optType=OptimizationType.MINIMIZATION, optFunc=MinMB, **kn): |
|
52
|
|
|
task = TaskConvPlot(D=D, nFES=nFES, nGEN=nGEN, optType=optType, benchmark=optFunc()) |
|
53
|
|
|
algo = alg(seed=seed[0], task=task) |
|
54
|
|
|
best = algo.run() |
|
55
|
|
|
logger.info('%s %s' % (best[0], best[1])) |
|
56
|
|
|
input('Press [enter] to continue') |
|
57
|
|
|
|
|
58
|
|
|
def getOptType(otype): |
|
59
|
|
|
if otype == OptimizationType.MINIMIZATION: return MinMB |
|
60
|
|
|
elif otype == OptimizationType.MAXIMIZATION: return MaxMB |
|
61
|
|
|
else: return None |
|
62
|
|
|
|
|
63
|
|
|
if __name__ == '__main__': |
|
64
|
|
|
pargs, algo = getDictArgs(sys.argv[1:]), GlowwormSwarmOptimizationV1 |
|
65
|
|
|
optFunc = getOptType(pargs['optType']) |
|
66
|
|
|
if not pargs['runType']: simple_example(algo, optFunc=optFunc, **pargs) |
|
67
|
|
|
elif pargs['runType'] == 'log': logging_example(algo, optFunc=optFunc, **pargs) |
|
68
|
|
|
elif pargs['runType'] == 'plot': plot_example(algo, optFunc=optFunc, **pargs) |
|
69
|
|
|
|
|
70
|
|
|
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3 |
|
71
|
|
|
|
NiaOrg /
NiaPy
Loading history...