Community Detection and Disease identification using Meta-heuristic based Clustering methods

A Nature Inspired Hybrid Partitional Clustering Method Based on Grey Wolf Optimization and JAYA Algorithm

“…The problems of data clustering have been solved by using several conventional techniques; however, these techniques have more chances to converge to local optimal solutions due to the multi-modal nature of the problems. Therefore, to avoid local minima problems, most researchers have solved similar types of multi-modal problems such as time series forecasting [41], the FOPID-based damping controller [27], data-clustering problems [46,47,49], etc. by using natureinspired meta-heuristic algorithms.…”

“…Once an appropriate fitness function is chosen, the clustering algorithm can be converted into an optimization problem that minimizes the intra-cluster distances (compactness) and maximizes the inter-cluster distances (separability). Additionally, a partitional clustering algorithm can handle a large volume of data, which leads to more applications toward the field of research for grouping patterns for example, medical data analysis (for classifying positive and negative symptoms uniquely [46,47]), social network analysis (for identifying fake and real information or users), robotics (for classifying items or humans based on their body shape or activities), and market basket analysis (for classifying consumers according to their purchasing behaviors, etc.). In all of these applications, the nature of data items that are available as patterns are different from each other.…”

“…The recent literature reports that most of the meta-heuristic-based optimization algorithms are able to solve real-life optimization problems and those algorithms are such as: grey wolf optimization (GWO) [34], teaching learning-based optimization (TLBO) [44], bacteria foraging optimization (BFO) [17], the whale optimization algorithm (WOA) [33], the JAYA algorithm [43], ant colony optimization (ACO) [10], the sine cosine algorithm (SCA) [32], simulated annealing (SA) [29], the spotted hyena optimization algorithm (SHOA) [9], ant lion optimization (ALO) [31], particle swarm optimizations (PSO) [28], chemical reaction optimization (CRO) [30], differential evolution (DE) [50], etc. In recent times, most researchers have received immense interest in applying such nature-inspired algorithms on clustering problems [46][47][48][49]. Similarly, Jafer and Sivakumar [25] published a review article on a nature-inspired-based meta-heuristic optimization algorithm on ant-based clustering.…”