## 2 Literature Review
This hybrid algorithm is a combination of three metaheuristic algorithms: Particle Swarm Optimization (PSO) [46], Ant Colony Optimization (ACO) [47], and ABC. The algorithm consists of 3 steps that are executed sequentially. First, PSO is used to search for optimal thresholds, then ABC is used, and finally the ACO algorithm is used. It is not stated in the paper which PRNG was used in the execution of the metaheuristic algorithms. In [7], the ABC was hybridized with the GA to optimize parameter identification of an Escherichia coli-fed batch process model. First, the ABC algorithm is used to initialize the initial population for a given number of iterations. The resulting population is used as the initial population of GA and yields a population that is much closer to the optimal solutions than a randomly generated initial population. As in the previous related works, no mention is made of the PRNG utilized in the optimization process. The authors in [14] propose three hybrid metaheuristics based on the Grey Wolf Optimizer (GWO) [48] and WOA to optimize the dimensionality reduction process, an important solution to the dimensionality problem encountered in most machine learning methods. The first hybrid algorithm is executed in a sequential order, such that GWO is executed first, followed by WOA. In the second hybrid algorithm, the optimization process randomly switches between two algorithms at each iteration. The third algorithm implements an adaptive switching mechanism between the GWO and WOA based on the number of improved solutions within the population from the previous iteration. If the count of improved solutions in the population fails to surpass the designated threshold, the algorithm is replaced in the subsequent iteration. This third approach is closest to our new hybrid metaheuristic algorithm, which alternates the execution of the two algorithms during the optimization process, but in this approach, the selected PRNG that was used for the randomization phase is not mentioned. A hybrid metaheuristic algorithm based on a combination of the Ant Lion Optimizer (ALO) [49] and Jaya [50] algorithms was presented in [21]. The algorithm is divided into two phases. In the first phase, ALO and Jaya are run in parallel to obtain better solutions, which are then used in the second phase of the hybrid algorithm. In the second phase of the algorithm, solutions are selected from the population of the ALO and Jaya algorithms for each subsequent iteration, depending on the quality of each solution (value of the fitness function) in the previous iteration. This paper also does not mention the use of a specific PRNG that can improve the optimization results in the metaheuristic algorithm. In [15], a novel hybrid metaheuristic combining WOA and SA algorithms is proposed as a solution for reducing carbon emissions in a production distribution network. In this hybrid algorithm, SA is integrated with WOA, and instead of spiraling updates of positions in WOA, a local search based on SA is performed. In this way, the phases of intensification and diversification of WOA are improved. Again, there is no mention of the selected PRNG used in the randomization phase of the proposed algorithm. The authors in [22] proposed three hybrid algorithms for solving the optimization problem of maximizing the reliability of information retrieval in wireless multimedia sensor networks. The hybrid algorithms are based on GA and SA, where the branch-and-bound (B&B) algorithm [51] is used in the phases of the optimization process within GA or SA for a better optimization process. The use of the selected PRNG for the randomization process is also not mentioned in this paper. In [23], a hybrid metaheuristic based on the Cultural Algorithm (CA) [52] and Differential Evolution (DE) [53] was proposed. In each iteration, the participation ratio for both algorithms is calculated, which determines the influence of each algorithm on the optimization process. Also, the PRNG used for randomization in the optimization process is not specified in this paper.