Swarm Intelligence Techniques and Their Adaptive Nature with Applications - Complex System Modelling and Control Through Intelligent Soft Computations - page 258

proportional to the distance between the two nodes. Lastly, IWD needs to chose
path to next node from the multiple paths. Mechanism to chose path is that the IWD
prefers path with less soil. Hence, paths with less soil have higher chance to get
chosen by an IWD.
The strategy looks very similar to ACO. Pheromones are deposited through the
path an ant moves and with the time pheromone decreases as it evaporates. In case
of IWD, soil is removed from the path when an IWD moves through it. Only
difference is changes to the path is constant in case of ACO, whereas in case of
IWD these change are dependent on velocity and soil gained by an IWD. This
strategy can be

fitted to the generalized SI framework once we have initial soil and
velocity. In coming sections application and variation related issues with SI tech-
niques explained above are addressed, and also extended to other remaining SI
techniques in a generalized form.

4 Applicability of SI Techniques

An application is a composition of several sub-applications or modules. Each
module may be an explicit and working application. Considering the feature
selection problem of pattern recognition, it has several applications such as Medical
disease diagnosis (Selvaraj and Janakiraman 2013 ), salient object detection (Singh
et al. 2014 ) etc. Salient object detection can be used in surveillance systems (Graefe
and Efenberger 1996 ), image retrieval (Amit 2002 ; Gonzalez and Woods 2002 ),
advertising a design (Itti 2000 ) etc. All these abstract level applications in back-
ground require feature selection. Often intelligent techniques are used for feature
selection (Selvaraj and Janakiraman 2013 ; Singh et al. 2014 ). Similarly, several real
world applications at different level of abstraction require intelligent techniques in
background to solve associated problems. Sometime, intelligent techniques are
hybridized with classical methods (Ranaee et al. 2010 ). Hence, even if an appli-
cation in hand not directly incorporate intelligent techniques in it, alternatively can
be hybridized with classical approaches to improve ef ciency.

Application systems may have several control parameters that decide overall
behavior of the system. Behavior of any system can be depicted as function of control
parameters of that system. To optimize such functions optimization techniques are
utilized. Intuitively there may have two kinds of objectives behind using any opti-
mization technique. First one is about finding optimal values of the function for the

system. Second one is to find optimal settings of control parameters for the system.

Both objectives are interrelated, as optimal value of the function implies optimal
settings of control parameters. Normally it looks both are inseparable, but depending
on how the application will going to be bene

ted with the optimization techniques,
one can get clear indication about the notion of objective behind incorporation of such
techniques. Both kind of objectives can be understood with very general application
such as Traveling Salesman Problem (TSP) (Dorigo et al. 2006 ; Shah-Hosseini 2009 ).
Normally, main aim of TSP is to find shortest route to visit all cities. It is clear that