# Swarm Intelligence Algorithms for Feature Selection: A Review
## 8 Conclusions  
This paper presents a comprehensive survey of SI algorithms for FS, which reduces the gap in this research field. We first analyzed 64 SI algorithms systematically, and sorted them as per the proposed taxonomy. After that, the SI for FS was analyzed based on the SI framework. Different approaches are described in each framework phase of the SI algorithms. Moreover, to show the usage of FS in different application areas, we break down the presented papers in which researchers only used datasets for testing their methods and those who applied them to practical problems in the field. Based on the analyzed data, and many papers comprising SI for FS as shown in Figure 2, it can be seen that the interest in this field is increasing, because we live in the era of Big Data, which is challenging not only for FS, but for all machine learning techniques. Therefore, the growing tendency of many researchers is to select only the most informative features out of the whole set of several thousands or even ten thousands of features. Such high dimensionality of data is most evident in Medicine in the DNA microarray datasets. Another reason for the increased research efforts in this field is the result of the SI algorithms' boom in recent years, which were later applied for the FS problem. We think that the future in the SI field, not limited only to SI for FS, includes not only developing new algorithms based on the behavior of some animals, but also modifications of existing ones. The latter is expected to be done in the sense of combining different parts of algorithms together. Anyway, we strongly believe that the use of SI for FS will continue to grow and will provide effective and efficient solutions. Let this review serve as a foundation for further research in this field. Researchers can focus on comparing different SI algorithms and their variants, and prepare in-depth empirical and analytic research. It would also be interesting to look at the computational performance and time complexity of such algorithms and compare their efficiency. Because all of the novel algorithms must be tested, a systematization of datasets is recommended. As a basis, our division of datasets presented in Section 6.1 can be taken into account. We must also think in the direction of a unified measurement approach, which could improve comparison of the results. A more systematic approach to the evaluation of performance, advantages and drawbacks of specific algorithms would greatly improve the decision-making process of a data miner when facing the problem of feature selection. It would also be fruitful to compare whether some algorithms perform better in some specific cases, e.g., on specific datasets, small/large number of features, etc., and if their hybridization, e.g.,the introduction of some specific local search, affects all SI algorithms or just some of them. There is no obvious answer to what is the most effective SI algorithm in FS, for the following reasons. Firstly, the No Free Lunch Theorem [110] tells us a general-purpose, universal optimization strategy is impossible [111]. Furthermore, not all SI algorithms, as presented in Table 1, have been applied to the FS problems, and most of those that were lack good empirical evaluations. Experimental results obtained on different real-world problems are even harder to compare. For that reason, a systematical and exhaustive empirical investigation should be conducted in the future on how different algorithms, especially those which have shown the most promising results thus far, e.g., PSO, ACO, FA, GWO, DA, and GSO, and some promising newcomers, perform in varying conditions (on different datasets, for very big datasets, etc.). In this manner, the implementation issues and the impact of different configurations shall also be addressed. Nowadays, the majority of papers that address the feature selection problem use instances that have up to several thousand features.