# Swarm Intelligence Algorithms for Feature Selection: A Review
## 7 Discussion  
Alone within this survey, we analyzed 22 different original SI algorithms for FS and, altogether, 64 of their variants. However, for the vast majority of all applications, only a few of the algorithms are used. In this manner, the most frequently used algorithm (PSO) is used in almost 47% of all cases, while the four most frequently used algorithms cover more than three quarters of cases (79%), and the top seven algorithms even cover 90% of all cases (see Table 4). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **Publications** & **Citations** & **Share** \\ \hline PSO & 16 & 766 & 46.62\% \\ ACO & 6 & 226 & 13.76\% \\ BA & 2 & 154 & 9.37\% \\ ABC & 4 & 152 & 9.25\% \\ GWO & 7 & 123 & 7.49\% \\ FA & 5 & 49 & 2.98\% \\ WSO & 1 & 26 & 1.58\% \\ Others & 23 & 147 & 8.95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Impact (share of citations) of SI algorithms for FS. On the basis of the above, a reasonable doubt arises of whether one should even pursue the quest of analyzing the broad spectre of existing SI algorithms for FS. However, the fact is that some of the latest algorithms, e.g., BCO, CS, FA, and GWO, are also used in combination with some other techniques and present very promising results in FS. Some of the novel algorithms still provide enough improvements to be considered useful and produce an impact. Some of the selected algorithms and their components are sorted by proposed taxonomy in Table 5.  
Thus, the goal of this paper is to support a researcher in choosing the most adequate algorithm and its setting for his/her data mining tasks and endeavors. For this purpose, alongside the characteristics of abundance of SI algorithms for FS, it may well help to be aware of various practical implications of these algorithms and their use. As some researchers report different modifications, adaptations and hybridizations of the basic algorithms, which have shown promising practical results, we collected some of them. The introduction of different local search mechanisms is used to improve specific aspects of SI algorithms. For example, in [101], the Levy flight is included to avoid premature convergence of the Shuffled Frog Leaping Algorithm (SFLA) for cancer classification. In [102], on the other hand, the authors proposed to combine tabu search (TS) and binary particle swarm optimization (BPSO) for feature selection, where BPSO acts as a local optimizer each time the TS has been run for a single generation. In some cases, researchers combine specific characteristic of different SI algorithms to improve the overall efficiency. For example, in [103], the authors proposed a combination of Monkey Algorithm (MA) with Krill Herd Algorithm (KH) in order to balance the exploration and exploitation adaptively to find the optimal solution quickly. A similar case is presented in [81], where the diagnosis of childhood Atypical Teratoid/Rhabdoid Tumor (AT/RT) in Magnetic Resonance brain images and Hemochromatosis in Computed Tomography (CT) liver images has been improved through the hybridization of the Particle Swarm Optimization and Firefly (PSO-FF) algorithms. Normally, in typical machine learning application, FS is used to optimize at least two major objectives: predictive performance and the number of selected features. To solve this multi-objective optimization problem, in some cases, researchers used several co-evolving swarms--so-called multi-swarms. For example, in [104], the authors developed a formulation utilizing two particles' swarms in order to optimize a desired performance criterion and the number of selected features simultaneously to reduce the dimensionality of spectral image data. In [105], the authors proposed an SI FS algorithm, based on the initialization and update of only a subset of particles in the swarm, as they observed how feature selection failed to select small features subsets when discovering biomarkers in microarray data.