Swarm Intelligence Techniques and Their Adaptive Nature with Applications - Complex System Modelling and Control Through Intelligent Soft Computations - page 262

2013 ; Selvaraj and Janakiraman 2013 ). However, assigning of weights to each feature
is done through Decimal representation (Singh et al. 2014 ). Features in the vector
indicate whether they are selected or not with prede

ned threshold values. Each vector
represents a solution. Another example is in (Das et al. 2014 ), particles are considered
as individual network, which comprises links and weights of these links between two
neurons and transfer function. Presence and absence of link can be represented as
Boolean, weights can be represented as Decimal and transfer function may give
Boolean values. Hence author used both kinds of encoding to represent particles.
Improperly detected control parameters may not be suitable for the application and
may show unexpected results. Even if suitable control parameters are indenti ed, it is

a matter of judgment whether to encode as Decimal or Boolean. Sometimes, appli-
cations may have control parameters that can not be represented either as Decimal or
Boolean. In such cases needed some functions to transform them either to Decimal or
to Boolean. Moreover, some applications don

' t even have clear control parameters.

Implementer needs to be identi ed suitable control parameters from the application in

those cases.

6 Strategic Changes

Over the decades SI techniques have been undergone several strategic changes to
improve ef ciency, reliability, scalability and solution quality. Those changes were

carried out irrespective of applications. Often used benchmark functions to test
those techniques. But, when those improved variants are applied to some real life
problems, found that they are not comply with the problem. It seems that they
perform better on some speci

c problems. Even though performed well on
benchmark functions, it cannot be generalize that same technique will perform
better in all problems as stated in no free lunch (NFL) theorem (Wolpert and
Macready 1997 ). Same method may show best result on some problem or even on
standard benchmark functions but may show worst result in other problems. Hence,
problem speci c improvements were done to suit real life problem, but always have

exceptions. For example PSO often found better to feature selection when
hybridized with other approaches, but on the contrary hybridized ACO shows
degraded results on the same problem (Kothari et al. 2012 ).
Introduction of new strategy to the existing technique have added extra overhead
to the technique. Though shows better results than previous actual techniques, they
become more complex. Implementer has to look over more constraints in order to
realize actual variant of the technique. If we take a look at strategic changes of PSO,
it has undergone several modi cations to the PSO originally introduced by Ken-

nedy and Eberhart ( 1995 ). Following the original version of PSO discrete variant
was also introduced by Kennedy and Eberhart ( 1997 ). Concept of constriction
factor was introduced by Cleric and Kennedy ( 2002 ). Fully informed PSO (FIPS)
was proposed by Mendes ( 2004 ), where every particle is informed about others
experience. This requires too high computation time. Linearly varying coef

cients