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Each kind of optimization problem can be solved very ef
ciently with some
speci
c techniques. These techniques include mathematical approaches such as
linear programming (Matou
artner 2007 ; Todd 2002 ; Wiki 2014 ), non-
linear programming (Borwein and Lewis 2010 ; Ruszczynski 2006 ) and iterative
techniques (Wiki 2014 ) as well as numerous heuristic approaches such as Evolu-
tionary Algorithms (Rechenberg 1994 ; Schwefel 1994 ; Yan et al. 2005 ), Genetic
Algorithms (Deb et al. 2002 ; Holland 1975 ), Swarm Intelligence (Dorigo 1992 ;
Karaboga 2005 ; Kennedy and Eberhart 1995 ; Rashedi et al. 2009 ; Shah-Hosseini
2008 , 2009 ) and other nature inspired methods. In these days heuristic approaches
have gained popularity specially Genetic Algorithms and Swarm Intelligence
techniques. Applications of almost all
š
ek and G
ä
fields utilize swarm intelligence techniques in
their speci
c problems. As mentioned above, swarm intelligent techniques are uti-
lized mainly for solving optimization problem. These techniques are inspired from
chemical, biological and physical phenomenon of nature. Extensively used
approaches in various applications include Particle Swarm Optimization (PSO)
(Kennedy and Eberhart 1995 ), Ant Colony Optimization (ACO) (Dorigo 1992 ),
Arti
cial Bee Colony (ABC) (Karaboga 2005 ), Gravitational Search Algorithm
(GSA) (Rashedi et al. 2009 ) and Intelligent Water Drop (IWD) (Shah-Hosseini
2008 , 2009 ). In this chapter we will try to cover these popular swarm based tech-
niques in perspective of their applicability to numerous problems. These techniques
have undergone several changes. We will study those changes with respect to their
applications and try to draw key issues behind such changes. Variety of applications
of different domains as well as frequent variation within the technique create chaos.
Induced several confusions regarding selection of suitable technique for the appli-
cation. We will try to address those issues and summarize them in a generalized
manner for all the applications. We will also try to generalize issues regarding
variations of techniques and their applicability throughout the chapter.
Rest of the chapter is organized as follows: Section 2 surveys various works
which presents applications of swarm based techniques and extrapolates trade off
between applications and swarm based techniques. Section 3 provides brief intro-
duction to popular swarm based approaches and generalize these techniques into
common framework. Section 4 addresses issues related objectives behind incor-
poration of swarm based approaches, probable plug points in any application and
suitable problem types. Considering these constraints a generalized framework is
presented for any application and such techniques. Section 5 presents various
encoding schemes of applications to
fit swarm based techniques and encoding
related changes in techniques. Section 6 explains strategic changes of swarm based
techniques. Section 7 illustrates parameter tuning related issues of swarm based
techniques. Section 8 discusses various application related problems, advantages
and dif
culties. Finally, concluded in Sect. 9 .
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