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have constraints for those cases require additional parameters to
fit the technique in
it. Hence, such application speci
c parameter tunings cannot be generalized for
other applications.
To account maximum application domains for any SI technique, require gen-
eralized parameters settings which can be used in any application and also needs to
ensure that those setting will result better. So, most of such tunings are carried out
with benchmark functions to test tuned parameter. Benchmark function based
parameter adjustments can be considered as generalized settings for applications of
different domain.
Generalized parameter tuning can be of two kinds, constant value based tuning
and strategic tuning. Universally used PSO have three parameters C 1 , C 2 ,
. Initial
version of PSO had only two parameters C 1 and C 2 . Later on suggested one more
parameter because, uncontrolled velocity often led to move particles much ahead of
optimal solution as expected, which implies divergence of particles from the
objective, resulting slow convergence of the process to the optimal solution. Shi and
Eberhart ( 1998a ) has observed that velocity of particle has to be controlled in order
to control search scope of particle. To overcome this problem they introduced new
parameter inertia weight to control velocity of particle. Cleric and Kennedy has
done similar control by introducing constriction factor c instead of inertia weight
(Clerc and Kennedy 2002 ). They showed values of two coef
x
cient have to be
1.4962, while inertia weight has to be 0.7968. Kennedy and his colleague (Kennedy
et al. 2001 ) shows value of C 1 and C 2 has to be 2. Apart from these constant value
based tuning, parameters of PSO also tuned strategically to act more friendly to the
applications of different domains. Shi and Eberhart ( 1999 ) has varied inertia weight
x
linearly. Sometime added new parameters which might be for realization of
strategic tuning. For example, to vary
x
of PSO there has to be upper bound
x up
and lower bound
x low . These newly introduced parameter also has to be tuned to
obtain better range of
x
for varying linearly. Shi and Eberhart ( 1998b ) showed
x low are 0.9 and 0.4 respectively. Hence, tuning of parameters not
only done as constant value based or strategic tuning, but also can be done both
simultaneously. Parameter values obtained through constant value based tuning
may not suit some application, but strategic tuning can
x up and
values of
fit all application which
utilizes SI technique.
8 Discussion
Adaptation have to be done both from application side as well as SI techniques to
comply with each other. In order to
fit SI techniques into diverse applications of
different domains have to be generalized. Such generalization has to be done in SI
techniques with respect to any application or benchmark problems. Generally,
control parameters of application varies with different application domain and the
environment of the application. SI techniques
'
parameters and strategies have to be
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