# Nature-Inspired Optimization Algorithms for Text Document Clustering--A Comprehensive Analysis
## 2 Related Works
### Other Algorithms  
In Reference [38], the ABC search equation is improved and integrate two local search paradigms into the standard ABC, called chaotic local search and gradient search to enhance its performance. The proposed method is called chaotic gradient artificial bee colony. Three separated benchmark text datasets, namely Reuters-21,578, Classic4, and WebKB, are evaluated the efficiency of the introduced method. The results obtained are contrasted with ABC, a recent variant of ABC, including gbest-guided ABC, a variant of the methodology suggested, called chaotic artificial bee colony, memetic ABC, and K-means standard clustering technique. The experimental results show promising outcomes in terms of solution consistency and speed of convergence. In Reference [85], an approach using fuzzy logic techniques and self-organizing maps (SOM) is presented to handle conceptual aspects in document clusters and to minimize training time, a concept frequency formula is applied in order to calculate the degree of presence of a concept in a document. For the calculation of the polysemic degree of terms and the synonymic degree between terms, this formula is focused on new fuzzy equations. New fuzzy enhancements such as automatic topology selection, initialization of the heuristic map, a fuzzy similarity measure and an extraction method for keywords are employed in this method. In order to compare the introduced method with classic SOM approaches via Reuters selection, some experimental tests were carried out. The efficiency of the method has been evaluated in terms of F-measurement and training time. The experiments revealed that, compared to classic SOM strategies, the introduced method achieves satisfactory results with less training time. Text analysis involves complex techniques for managing different text documents in the field of text mining. machine recruitment and pattern recognition. Computers may begin to organize a corpus document using rational text-clustering methods in some organizational frameworks of conceptual clusters. noisy, inconsequential and superfluous characteristics are included in the informative and un-informative functionality of text documents. The unsupervised selection of text features is the key method of determining a new subset of informative features for each document. There are two goals of the functional selection technique: (1) optimize the reliability of the text clustering algorithm, (2) minimize the number of uninformative traits. In Reference [37], a new technique proposed is that it achieves a mature convergence rate and needs minimal computational time and is stuck in a low dimensional space in local minima. As the input and pre-processing steps are conducted in the document, the text data is fed. Next, by selecting the local optima from the text document and then choosing the proper global optima from the local optimum utilizing hybrid GWO-GOA, the text function selection is analyzed. In addition, the chosen optima are clustered to use of Fuzzy c-means (FCM) clustering algorithm. the proposed algorithm increases reliability and reduces the time cost. In the introduced method, eight datasets have been used and the performance is essentially predicted. The performance measures used for conducting text feature selection and text clustering are accuracy, precision, recall, F-measure, sensitivity, specificity and demonstrated better quality when comparing with numerous methods. The proposed method showed 87.6. In several main fields of information retrieval including text mining, and natural language processing, text clustering problem (TCP) is a main method. This poses to the need for a powerful method for document clustering that can be utilized efficiently to navigate, summarize, and organize data to gather large data sets. Reference [86] provided an adaptation of the grey wolf optimizer (GWO) for TCP (TCP-GWO). Above what is possible with metaheuristic swarm-based techniques, the TCP requires a high degree of accuracy.