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Figure 1. The flowchart of the sequential optimization method
this method is also an open question. The flowchart
of SOM is presented in Figure 1.
The PSO algorithm, which is based on the
study of social behaviour in a self-organized
population system (i.e., ant colonies, fish schools),
searches a space by adjusting the trajectories of
so-called particles. Every particle is characterized
by the vector of particle position p i and the vec-
tor of particle velocity v i . In this paper, the posi-
tion vector p i of the i i-th particle contains the main
damping coefficients of dampers currently
mounted on the structure, i.e.,
p i
( )
1 2 . The dimension of the
vector p i is equal to the number of acceptable
damping locations. The search for an optimal
solution is performed by updating the subsequent
positions of particles. Moreover, every particle
keeps record of its best fitness achieved so far as
the vector b i and the best fitness and correspond-
ing solution achieved in the particle's neighbour-
hood as the vector g i .
= [
c
( )
i
,
c
( )
i
, ....,
c
i
]
T
d
,
d
,
d m
,
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