/
_repeat_spso.py
51 lines (41 loc) · 2.13 KB
/
_repeat_spso.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
"""Repeat the following paper for `SPSO`:
Shi, Y. and Eberhart, R., 1998, May.
A modified particle swarm optimizer.
In IEEE World Congress on Computational Intelligence (pp. 69-73). IEEE.
https://ieeexplore.ieee.org/abstract/document/699146
Kennedy, J. and Eberhart, R., 1995, November.
Particle swarm optimization.
In Proceedings of International Conference on Neural Networks (pp. 1942-1948). IEEE.
https://ieeexplore.ieee.org/document/488968
Luckily our Python code could repeat the data generated by the other Python code *well*.
Therefore, we argue that its repeatability could be **well-documented**.
You can run the following Python script (note that first install `pymoo` via `pip install pymoo`):
--------------------------------------------------------------------------------------------------
from pymoo.algorithms.soo.nonconvex.pso import PSO
from pymoo.problems.single import Ackley
from pymoo.optimize import minimize
problem = Ackley(n_var=100)
algorithm = PSO(pop_size=20)
res = minimize(problem=problem, algorithm=algorithm, termination=('n_eval', 1e6), verbose=True, seed=1)
print("Best-so-far solution found: F = %s" % (res.F)) # F = [0.19331169]
"""
import time
import numpy as np
from pypop7.benchmarks.base_functions import ackley
from pypop7.optimizers.pso.spso import SPSO as Solver
if __name__ == '__main__':
start_run = time.time()
ndim_problem = 100
for f in [ackley]:
print('*' * 7 + ' ' + f.__name__ + ' ' + '*' * 7)
problem = {'fitness_function': f,
'ndim_problem': ndim_problem,
'lower_boundary': -32.768*np.ones((ndim_problem,)),
'upper_boundary': 32.768*np.ones((ndim_problem,))}
options = {'max_function_evaluations': 1e6,
'seed_rng': 0,
'verbose': 1e3}
solver = Solver(problem, options)
results = solver.optimize()
print(results) # 4.3076653355456074e-14 vs 0.19331169 (from pymoo)
print('*** Runtime: {:7.5e}'.format(time.time() - start_run))