In this paper it is discussed and briefly experimentally investigated the performance of multi-swarm PSO with super-sized swarms. The selection of proper population size is crucial for successful PSO using. This work follows previous promising research.

1.
Kennedy
,
J.
,
Eberhart
,
R.
:
Particle swarm optimization
. In:
IEEE International Conference on Neural Networks
,
1995
, pp.
1942
1948
.
2.
Kennedy
,
J.
,
Eberhart
,
R.C.
,
Shi
,
Y.
:
Swarm Intelligence
.
Morgan Kaufmann Publishers
, (
2001
).
3.
Nickabadi
,
A.
,
Ebadzadeh
,
M.M.
,
Safabakhsh
,
R.
:
A novel particle swarm optimization algorithm with adaptive inertia weight
.
Applied Soft Computing
11
(
4
),
3658
3670
(
2011
).
4.
Yuhui
,
S.
,
Eberhart
,
R.
:
A modified particle swarm optimizer
. In:
IEEE World Congress on Computational Intelligence
.,
4-9 May 1998
, pp.
69
73
.
5.
Pluhacek
,
M.
;
Senkerik
,
R.
;
Zelinka
,
I.
,, ‘
The Initial Study on the Potential of Super-Sized Swarm in PSO
’, in
Advances in Intelligent Systems and Computing, Mendel 2015
, Volume
378
,
2015
, pp
127
135
6.
Liang
,
J.
,
Suganthan
,
P.N.
:
Dynamic multi-swarm particle swarm optimizer
. In:
Swarm Intelligence Symposium, 2005. SIS 2005
, pp.
124
129
.
7.
Liang
JJ
,
Qu
B-Y.
,
Suganthan
PN
,
Hernández-Díaz
AG
(
2013
)
Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session and Competition on Real-Parameter Optimization
, Technical Report 201212,
Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University
,
Singapore
8.
Kotyrba
,
M.
 Influence of Changes in Initial Conditions for the Simulation of Dynamic Systems In
T. E.
Simos
and
C.
Tsitouras
(eds.)
AIP Conference Proceedings
. Volume
1648
, eid 550003, ISBN: 978-0-7354-1287-3., ISSN: ,
2015
.
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