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addition of new parameters and tuning of various parameters have often been sur-
veyed (Kameyama 2009 ), sometime such study extended to various applications.
Intuition behind introduction of a new technique to solve any problem is to apply
in real life problems and solve associated problems more ef
ciently. Hence, gen-
eralized study would be more realistic and helpful to the society of diverse appli-
cation domain rather than any particular application speci
c study. SI techniques in
particular covers huge application domain. In this case, generalized study with
comparative analogy to the applications of multiple domain will be more helpful.
Implementer of any domain can have an intuitive idea about applicability of such
techniques into the speci
c applications of any domain.
3 Swarm Intelligences
Swarm intelligent (SI) techniques are heuristic stochastic search processes. SI
approaches can be generalized as follows: all approaches are initiated with a set of
solutions called population, then in successive steps each candidate of the set learns
collectively from other candidates and adapts itself in accordance to the solution
space. Strategy incorporated and learning mechanism of these techniques mostly
mimic the natural facts and phenomena. Such nature inspired mathematical models
can be plugged into one framework. In this section we will brief popular SI tech-
niques and try to wrap up in one generalized framework.
3.1 Particle Swarm Optimization
Particle Swarm Optimization (PSO) originally introduced by Kennedy and Eberhart
in 1995 (Kennedy and Eberhart 1995 ). Basic intuition behind PSO was simulation
of cooperative learning mechanism of bird
'
s
fl
flocking. Flying birds in
fl
ock show
learning through individual
'
s experience and follow other. One of them leading the
fl
flock and other follows that leader. Once leader changes, immediately all other
individual including previous leader begin following it. This process continues until
reach their destination. Kennedy and Eberhart formulated this process into a
mathematical model with two very simple equations. One of those equations was
analogous to the position and other one was analogous to the velocity of bird or
particle. Experience of individual particle was conserved as personal best i.e. any
particle experienced best position so far. Experience of
ock or swarm or popu-
lation was conserved as global best i.e. the best position experienced by the swarm
so far. These experiences were used to learn and control velocity of particle.
Finally, particles moved to new position with learned velocity. So each particle in
solution space are associated with position and velocity. Velocity and position
equation proposed by Kennedy and Eberhart in original PSO are shown below:
fl
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