# Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering
## 4 Literature Review  
In this section, we made a general review about the methods using optimization fuzzy clustering with different bio-inspired optimization methods. However, in the following sections a deep study is developed by doing specific queries to Web of Science, and the VOSviewer tool is used to calculate the clusters of the analyzed works. In Table 1, is presented a list with the most popular bio-inspired optimization algorithms based on swarms, physics, populations, chemistry, and evolution. This table shows many methods in chronological orders that have been used since 1975 to date. However, only there are some methods, but can be useful to expand the knowledge about these methods and to observe the inspiration type. We made the query from Web of Science: \"Optimization fuzzy clustering\", we found a total of 2208 papers with this topic. However, in this paper only a description of the most recent works is presented, but with the query above mentioned can be seen the updated works. Figure 2 shows the countries with greater number of publications. Additionally, to validate this study in the best way, with the collected dates, we applied a filter to calculate the number of papers and citations from 2011 to 2021. In total, were found 1752 records of Web of Science. With this available information, was possible to build a preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram described in Figure 3. It was the aim to observe with more detail the retrieved papers presented in Figure 2, with the main topic \"Optimization fuzzy clustering\", and using the \"citespace\" tool mentioned below in this paper. We built the network corresponding to flow diagram of Figure 3. Therefore, in Figure 4 it can be appreciated the network with a total of 456 nodes, 3137 links using 1717 qualified records. Furthermore, in this figure the clusters were automatically labeled using the keyword that appears most commonly in the search. We only reviewed information from Web of Science because it contains many relevant journals. However, the tools used in this review can also use information from Scopus or other available datasets, if required. Figure 2: Plot of 2225 papers of Web of Science for topic: (optimization fuzzy clustering) by countries. Recently, multi-view clustering research has attracted a lot of attention because of the rapidly increasing demand for unsupervised analysis of multi-view data in practical applications. In [21], a novel two-level weighted collaborative multi-view fuzzy clustering (TW-Co-MFC) approach was presented, to address the aforementioned issues. Figure 4: Network and clusters with a total of 456 nodes, 3137 links using 1717 qualified records. Figure 3: Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram with 1711 records included. Furthermore, in [22], authors proposed image segmentation using the bat algorithm with fuzzy C means clustering. The proposed segmentation technique was evaluated with existing segmentation techniques. On the other hand, in [23], the authors presented a hybridization of stud krill herd optimization with refined kernel fuzzy c-means clustering algorithm for efficient moving object exploration. Another recent study on this area is shown in [24], where the authors presented a hybrid semi-supervised interval type-2 possibilistic fuzzy c-means clustering and particle swarm optimization for optimizing results in satellite image analysis. Additionally, in [25] the glow-worm swarm algorithm was used for optimization of fuzzy based unequal clustering and context aware routing procedure. This was developed for random way point based dynamic wireless sensor networks. Based on fuzzy systems the unequal clustering is formed and the optimal cluster centroid is nominated to convey the information from cluster member to base station to increase the system lifespan and to decrease the energy consumption.