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school
fish, among others. Recently, its performance was enhanced by using a
multi-stage clustering procedure splitting the particles of the main swarm over a
number of sub-swarms based upon the values of objective functions and the par-
ticles positions (Nickabadi et al.
2012
), utilizing multiple ranking criteria to de
ne
three global bests of the swarm as well as employing fuzzy variables to evaluate the
objective function and constraints of the problem (Wang and Zheng
2012
),
employing an innovative method to choose the global and personal best positions to
enhance the rate of convergence and diversity of solutions (Mahmoodabadi et al.
2014b
), and using a self-clustering algorithm to divide the particle swarm into
multiple tribes and choosing appropriate evolution techniques to update each par-
ticle (Chen and Liao
2014
).
Lately, researchers have utilized hybrid optimization algorithms to provide more
robust optimization algorithms due to the fact that each algorithm has its own
advantages and drawbacks and it is not feasible that an optimization algorithm can
address all optimization problems. Particularly, Ahmadi et al. (
2013
) predicted the
power in the solar stirling heat engine by using neural network based on the hybrid
of genetic algorithm and particle swarm optimization. Elshazly et al. (
2013
) pro-
posed a hybrid system which integrates rough set and the genetic algorithm for the
ef
cation of medical data sets of different sizes and dimensionalities.
Abdel-Kader (
2010
) proposed an improved PSO algorithm for ef
cient classi
cient data clus-
tering. Altun (
2013
) utilized a combination of genetic algorithm, particle swarm
optimization and neural network for the palm-print recognition. Zhou et al. (
2012
)
designed a remanufacturing closed-loop supply chain network based on the genetic
particle swarm optimization algorithm. Jeong et al. (
2009
) developed a hybrid
algorithm based on genetic algorithm and particle swarm optimization and applied
it for a real-world optimization problem. Mavaddaty and Ebrahimzadeh (
2011
) used
the genetic algorithm and particle swarm optimization based on mutual information
for blind signals separation. Samarghandi and ElMekkawy (
2012
) applied the
genetic algorithm and particle swarm optimization for no-wait
flow shop problem
with separable setup times and make-span criterion. Deb and Padhye (
2013
)
enhanced the performance of particle swarm optimization through an algorithmic
link with genetic algorithms. Valdez et al. (
2009
) combined particle swarm opti-
mization and genetic algorithms using fuzzy logic for decision making. Premalatha
and Natarajan (
2009
) applied discrete particle swarm optimization with genetic
algorithm operators for document clustering. Dhadwal et al. (
2014
) advanced
particle swarm assisted genetic algorithm for constrained optimization problems.
Bhuvaneswari et al. (
2009
) combined the genetic algorithm and particle swarm
optimization for alternator design. Jamili et al. (
2011
) proposed a hybrid algorithm
based on particle swarm optimization and simulated annealing for a periodic job
shop scheduling problem. Joeng et al. (
2009
) proposed a sophisticated hybrid of
particle swarm optimization and the genetic algorithm which shows robust search
ability regardless of the selection of the initial population and compared its capa-
bility to a simple hybrid of particle swarm optimization and the genetic algorithm
and pure particle swarm optimization and pure the genetic algorithm. Castillo-Villar
et al. (
2012
) used genetic algorithm optimization and simulated annealing for a
fl
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