## 2 Literature Review  
In the literature, there is a large number of proposed metaheuristic algorithms inspired by nature. Additionally, many authors have been working on modified versions of these algorithms. Recently, there has been a notable increase in the number of hybrid approaches among the proposed algorithms. These hybrid approaches typically combine two or more metaheuristic algorithms inspired by nature. By doing so, the optimization process for specific problems can be improved by addressing the weaknesses of each algorithm while capitalizing on their respective strengths. The first part of this review will focus on hybrid algorithms based on at least two nature-inspired metaheuristics. Scientific papers from the last ten years containing the terms _hybrid_, _nature-inspired_, and _metaheuristics_ were considered. These papers were retrieved from the Web of Science (WoS) database [37], which is considered the most valued scientific database in the academic community. The second part of this review will shift its focus toward PRNGs and their impact on the quality of the optimization process. In [10], a model for more accurate detection of melanoma on skin was presented. A hybrid metaheuristic algorithm consisting of a combination of Bat Algorithm (BA) [38] and Artificial Bee Colony (ABC) [39] is proposed to determine the optimal values of a set of parameters to improve the contrast and brightness of the image in order to better detect melanomas on the skin. In the search for optimal parameter values, first BA and then ABC are performed sequentially. Nowhere in the paper is it mentioned which PRNG was used to randomize the selected algorithm solutions. The authors in [11] proposed a novel hybrid metaheuristic algorithm (HAHA-SA) based on the artificial Hummingbird Algorithm (AHA) [40] and Simulated Annealing (SA) [41]. New HAHA-SA was applied to solve three constrained real-world design problems (planar ten-bar truss problem, planetary gear train problem, and car crashworthiness problem) to prove its quality. The authors did not mention how the hybrid algorithm was developed (parallel or sequential architecture or something else). It is also not stated which PRNG was used to randomize the selected algorithm solutions. In [9], a new hybrid metaheuristic algorithm based on the Water Cycle Algorithm (WCA) [42] and the Hookes and Jeeves Algorithm (H-J) [43] was presented. This novel hybrid algorithm was used for solving optimization problems in data clustering. The optimization process starts with the iterative execution of the WCA algorithm for the selected problem. If the WCA algorithm does not find a better solution in five consecutive iterations, the H-J algorithm is included in the optimization process, which then replaces the WCA algorithm. This paper also did not mention which PRNG is used to generate random solutions. For example, it would be possible, in case the WCA algorithm does not find a better solution in five consecutive iterations, to change the PRNG and thus avoid the local optimum and improve the optimization process for the given problem. In addition, two hybrid algorithms were proposed in [12] by combining a \"newer\" algorithm (e.g., the Red Deer Algorithm (RDA) [44] or the Whale Optimization Algorithm (WOA) [45]) and an \"older\", well-known algorithm (e.g., the Genetic Algorithm (GA) [8] or SA) to solve a problem of designing a supply chain network under uncertainty in the tire industry. The hybrid version called HWISA, which combines the WOA and SA algorithms in the search for the optimal solution, proved to be highly successful in solving this problem. In HWISA, SA is used in local search (intensification) throughout the optimization process with WOA. It is not mentioned in this paper which PRNG is used to generate random solutions. The selection of optimal thresholds in threshold-based image segmentation is proposed in [13] using a multi-stage hybrid metaheuristic algorithm.