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presented a parallel structure of the algorithm (i.e., parallel CSO or PCSO). They
further developed an enhanced version of their PCSO (EPCSO) by incorporating
the Taguchi method into the tracing mode process of the algorithm (Tsai et al.
2012). The binary version of CSO (BCSO) was developed by Sharafi et al. (2013 )
and applied to a number of benchmark optimization problems and the zero–one
knapsack problem. The chaotic cat swarm algorithm (CCSA) was developed by
Yang et al. (2013a). Using different chaotic maps, the seeking mode step of the
algorithm was improved. Based on the concept of homotopy, Yang et al. (2013b),
proposed the homotopy-inspired cat swarm algorithm (HCSA) in order to improve
the search efficiency. Lin et al. (2014a) proposed a method to improve CSO and
presented the Harmonious-CSO (HCSO). Lin et al. (2014b) introduced a modified
CSO (MCSO) algorithm capable of improving the search efficiency within the
problem space. The basic CSO algorithm was also integrated with a local search
procedure as well as the feature selection of support vector machines (SVMs). This
method changed the concept of cat alert surroundings in the seeking mode of CSO
algorithm. By dynamically adjusting the mixture ratio (MR) parameter of the CSO
algorithm, Wang (2015) enhanced CSO algorithm with an adaptive parameter
control. A hybrid cat swarm optimization method was developed by Ojha and
Naidu (2015) through adding the invasive weed optimization (IWO) algorithm to
the tracing mode of the CSO algorithm.
Several other authors have used CSO algorithm in different fields of research on
optimization problems. Lin and Chien (2009) constructed the CSO algorithm + SVM
model for data classification through integrating cat swam optimization into the SVM
classifier. Pradhan and Panda (2012) proposed a new multiobjective evolutionary
algorithm (MOEA) by extending CSO algorithm. The MOEA identified the non-
dominated solutions along the search process using the concept of Pareto dominance
and used an external archive for storing them. Xu and Hu (2012) presented a
CSO-based method for a resource-constrained project scheduling problem (RCPSP).
Saha et al. (2013) applied CSO algorithm to determine the optimal impulse response
coefficients of FIR low pass, high pass, bandpass, and band stop filters to meet the
respective ideal frequency response characteristics. So and Jenkins (2013)usedCSO
for Infinite Impulse Response (IIR) system identification on a few benchmarked IIR
plants. Kumar et al. (2014) optimized the placement and sizing of multiple distributed
generators using CSO. Mohamadeen et al. (2014) compared the binary CSO with the
binary PSO in selecting the best transformer tests that were utilized to classify
transformer health, and thus to improve the reliability of identifying the transformer
condition within the power system. Guo et al. (2015)proposedanimprovedcat
swarm optimization algorithm and redefined some basic CSO concepts and opera-
tions according to the assembly sequence planning (ASP) characteristics. Bilgaiyan
et al. (2015) used the cat swarm-based multi-objective optimization approach to
schedule workflows in a cloud computing environment which showed better per-
formance, compared with the multi-objective particle swarm optimization (MOPSO)
10 M. Bahrami et al.