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In this study, Mountain Gazelle Optimization (MGO) and Gazelle Optimization Algorithm (GOA) algorithms, which have been newly proposed in recent years, were examined. Although MGO and GOA are different heuristic algorithms, they are often considered the same algorithms by researchers. This study was conducted to resolve this confusion and demonstrate the discovery and exploitation success of both algorithms. While MGO developed the exploration and exploitation ability by being inspired by the behavior of gazelles living in different groups, GOA model was developed by being inspired by the behavior of gazelles in escaping from predators, reaching safe environments and grazing in safe environments. MGO and GOA were tested on 13 classical benchmark functions in seven different dimensions and their success was compared. According to the results, MGO is more successful than GOA in all dimensions. GOA, on the other hand, works faster than MGO. Additionally, MGO and GOA were tested on three different engineering design problems. While MGO was more successful in the tension/compression spring design problem and welded beam design problems, GOA achieved better results in the pressure vessel design problem. The results show that MGO improves the ability to explore and avoid local traps better than GOA. MGO and GOA are also compared with three different heuristic algorithms selected from the literature (GSO, COA, and ZOA). According to the results, MGO has shown that it can compete with new algorithms in the literature. GOA, on the other hand, lags behind comparison algorithms. [ABSTRACT FROM AUTHOR]

Today, with the increasing use of technology tools in daily life, big data has gained even more importance. In recent years, many methods have been used to interpret big data. One of them is metaheuristic algorithms. Meta-heuristic methods, which have been used by very few researchers yet, have become increasingly common. In this study, Tunicate Swarm Algorithm (TSA), which has been newly developed in recent years, was chosen to solve big data optimization problems. The Enhanced TSA (ETSA) was obtained by first developing the swarm action of the TSA. In order to show the achievements of TSA and ETSA, various classical benchmark functions were determined from the literature. The success of ETSA has been proven on these benchmark functions. Then, the successes of TSA and ETSA are shown in detail on big datasets containing six different EEG signal data, with five different population sizes (10, 20, 30, 50, 100) and three different stopping criteria (300, 500, 1000). The results were compared with the Jaya, SOA, and SMA algorithms selected from the literature, and the success of ETSA was determined. The results show that ETSA has sufficient success in solving big data optimization problems and continuous optimization problems. [ABSTRACT FROM AUTHOR]