Particle swarm optimization (PSO) is a classical metaheuristic algorithm. The initial form of PSO may not suitable for several optimization problems of structural engineering. A basic modification of PSO is the usage of an inertia function in order to adjust the contribution of existing values of the velocity. In several problems, the inertia function is necessary in order to keep the candidate possible solution in a possible range without an extreme increase of the velocity value. This situation is proved by employing PSO for a basic topology optimization of a truss structure. The problem is solved by using constant inertia functions from 0.1 to 1 with 0.1 increments. The inertia function is crucially effective in the performance, sensibility and computational.

1.
Kennedy
,
J.
,
Eberhart
,
R.C.
,
1995
.
Particle swarm optimization
. In: Proceedings of
IEEE International Conference on Neural Networks No. IV
, November 27-December 1, pp.
1942
1948
, Perth Australia.
2.
Schutte
,
J. F.
and
Groenwold
,
A. A.
(
2003
),
Sizing design of truss structures using particle swarms
,
Structural and Multidisciplinary Optimization
, Vol.
25
, No.
4
, pp.
261
269
, DOI:
3.
Perez
,
R. E.
and
Behdinan
,
K.
(
2007
),
Particle swarm approach for structural design optimization
,
Computers & Structures
, Vol.
85
, No.
19
, pp.
1579
1588
, DOI:
4.
Li
,
L. J.
,
Huang
,
Z. B.
,
Liu
,
F.
and
Wu
,
Q. H.
(
2007
),
A heuristic particle swarm optimizer for optimization of pin connected structures
,
Computers & Structures
, Vol.
85
, No.
7
, pp.
340
349
, DOI:
5.
Kaveh
,
A.
and
Talatahari
,
S.
(
2009
),
Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures
,
Computers & Structures
, Vol.
87
, No.
5
, pp.
267
283
, DOI:.
6.
Dorigo
,
M.
,
Maniezzo
,
V.
,
Colorni
,
A.
,
1996
.
The ant system: Optimization by a colony of cooperating agents
.
IEEE Transactions on Systems Man and Cybernet B
26
,
29
41
.
7.
Yang
,
X. S.
,
2008
.
Nature-Inspired Metaheuristic Algorithms
,
Luniver Press.
8.
Yang
,
X. S.
, &
Deb
,
S.
(
2009
, December). Cuckoo search via Lévy flights. In
Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on
(pp.
210
214
).
IEEE.
9.
Yang
,
X. S.
(
2010
).
A new metaheuristic bat-inspired algorithm
.
Nature inspired cooperative strategies for optimization (NICSO 2010)
,
65
74
.
10.
Xin-She
Yang
, Flower pollination algorithm for global optimization, in:
Unconventional Computation and Natural Computation 2012
,
Lecture Notes in Computer Science
, Vol.
7445
, pp.
240
249
(
2012
).
11.
Gandomi
,
A.H.
,
Alavi
,
A.H.
,
2012
.
Krill Herd: A New Bio-Inspired Optimization Algorithm
.
Communications in Nonlinear Science and Numerical Simulation
DOI: .
12.
Karaboga
,
D.
(
2005
). An idea based on honey bee swarm for numerical optimization (Vol.
200
).
Technical report TR06
,
Erciyes University.
Turkey.
13.
Yang
,
X. S.
(
2005
).
Engineering optimizations via nature-inspired virtual bee algorithms
.
Lecture notes in comuter science
,
3562
,
317
323
.
14.
Karaboga
,
D.
, &
Basturk
,
B.
(
2008
).
On the performance of artificial bee colony (ABC) algorithm
.
Applied soft computing
,
8
(
1
),
687
697
.
15.
Majid
,
K. I.
(
1974
). Optimum design of structures.
Newnes-Butterworth
,
London.
16.
Nigdeli
SM
,
Bekdaş
G
,
Yang
X-S
(
2016
). Application of the Flower Pollination Algorithm in Structural Engineering, In:
Metaheuristics and Optimization in Civil Engineering
,
Yang
X-S.
,
Bekdaş
G.
,
Nigdeli
S.M.
, Eds.,
Springer
, pp.
25
43
, 2016.
17.
Li
,
J. P.
,
Balazs
,
M. E.
,
Parks
,
G. T.
(
2007
),
Engineering design optimization using species-conserving genetic algorithms
,
Eng Optmiz
39
(
2
),
147
161
18.
Gandomi
,
A. H.
,
Yang
,
X. S.
, and
Alavi
,
A. H.
(
2013
),
Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems
,
Engineering with Computers
,
29
,
17
35
.
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