Information Technology Reference
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complexity. Hence, these problems require approximated solution which is relatively
good. SI techniques resulted high quality solution to those problem (Dorigo et al.
2006 ; Shah-Hosseini 2009 ). Real life application often experiences such problem. In
power system applications, problems such as optimal power
flow is a NP-hard
problem (Alzalg et al. 2011 ). As explained above, an application have several
modules. Those modules may be of different kind of problems. Some of those
problems may be of NP-hard category. Summarily, one can get an indication to adopt
SI techniques to solve those problems. For example clustering is a NP-hard problem
(Kulkarni and Venayagamoorthy 2011 ). Many application of various domains
such as social network analysis (Honghao et al. 2013 ; Kumar and Jayaraman 2013 ),
image processing (Hancer et al. 2012 ), wireless sensor network (Kulkarni and
Venayagamoorthy 2011 ) used clustering for their respective problems. This is
notable that all these applications adopt SI techniques related to clustering. Hence,
detection of problem complexity can be used as good indicator to decide whether
associated application needed to grasp SI technique to solve the problem. Objective
behind such grasping may vary with problem to problem and can be decided on the
basis of problem. As mentioned above, clear objective and problem complexity of
associated problem can be handy to decide on applicability of SI techniques.
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5 Encoding Schemes
SI techniques act as black box to the application and to implementers act as white
box. These techniques take control parameters and
fitness function of an application
as input and gives optimal solutions as output. Irrespective of the application,
optimization techniques will result solution to any input. Those results may be
suitable for the application or may not be. It is the implementer who can judge and
manipulate the inputs with respect to the considered technique. Results are very
much dependent on representative inputs of the application to the SI technique.
Hence, representation of control parameters of an application in terms of input to
any SI technique is very important. Decision on such issues are very sensitive to the
application.
SI techniques use inputs to evaluate
fitness or objective function that effects
control parameters of application and this function has to be optimized. Generally SI
techniques use Boolean or Decimal values to evaluate
fitness function. If applica-
tion
s control parameters are not in the same form as the considered technique, then
control parameters have to be encoded so that it become required kind of values that
are to be used by the technique. Otherwise, application control parameters can be used
directly as input to the SI technique. The generalized mechanism of encoding scheme
is presented in Fig. 3 with suitable examples. When application control parameter
values are Boolean, actually in this case value of any control parameter indicates its
selection if value is 1 and deselection if value is 0. During encoding into Decimal,
control parameter values are taken together as Boolean string. All control parameter
values can be considered at once to represent a single Boolean string or grouped into
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