## 2 Literature Review
The hybrid metaheuristic approach based on the Mean Gray Wolf Optimizer (MGWO) [54] and WOA is proposed in [24]. In this hybrid algorithm, WOA is extended by a spiral equation from MGWO with the aim of achieving a balance between the exploitation and exploration phases. As in all previous works, the authors of this hybrid algorithm do not specify which PRNG is used for randomization. The authors in [25] presented the Hybrid Flower Pollination Algorithm (HFPA), which is used for the optimal design of wideband digital differentiators and digital integratorswith infinite impulse response. This hybrid algorithm is based on the PSO and Flower Pollination Algorithm (FPA) [55] algorithms. In this hybrid solution, PSO and FPA are run sequentially, with the best solutions of the PSO algorithm as the initial solutions for the FPA algorithm. The paper does not specify which PRNG was used. In [26], three nature-inspired metaheuristics were combined in a new hybrid approach to solve constrained problems in numerical and engineering optimization. These metaheuristics are PSO, GA, and Symbiotic Organisms Search (SOS) [56]. The optimization process starts with GA, where better solutions have a higher probability of advancing to the second optimization stage as input solutions for the PSO algorithm. The principle is similar when moving from the PSO algorithm to SOS, where better solutions have a higher chance of being part of the initial population in SOS. It is not known which PRNG is chosen for the randomization phases within the hybrid algorithm. The mean Gbest Particle Swarm Optimization (MGBPSO) [27] and Gravitational Search Algorithm (GSA) [57] were combined into a novel hybrid metaheuristic approach in [27] to improve the solution for function optimization in search space. GSA and MGBPSO are executed in parallel, and the results of both algorithms are incorporated into the optimization result. In this way, the strengths of each algorithm are effectively utilized in the hybrid version, namely the exploitation phase of the MGBPSO algorithm and the exploration phase of the GSA algorithm. The PRNG used in the new hybrid algorithm is not mentioned in this paper. The authors in [28] proposed a hybrid variant of the ABC algorithm, where the functionality of the exploitation phase is enhanced by a newly proposed simple local search technique (SLST). The SLST technique improves the local search in the search space during the process of intensifying good solutions. Again, the selected PRNG was not mentioned in the paper. In [29], two hybrid metaheuristic approaches are proposed. Both approaches are based on ACO and FPA algorithms. In the first version of the hybrid algorithm, the FPA algorithm was integrated with the ACO algorithm to improve the exploration phase. In the second version of the hybrid algorithm, ACO and FPA are executed sequentially and completely independently. It is not known which PRNG was used for the randomization process within the proposed algorithms. A new hybrid metaheuristic based on PSO and Harmony Search Algorithm (HS) [58] is proposed in [30]. In this algorithm, HS and PSO are executed sequentially. HS is executed first, and its solution population represents the solution population for each iteration of the PSO algorithm. The PRNG used is not specified. The authors in [31] propose novel hybrid metaheuristic based on GA and ACO for optimizing the resource levelling problem. The main idea behind the integration of ACO and GA is to use the ACO algorithm, which focuses on discovering a high-quality local solution. Then, the algorithm GA uses the solution obtained from ACO to search for the global optimum. The PRNG used in the optimization process was also not mentioned in this paper. In [32], a new hybrid nature-inspired metaheuristic approach for optimizing course scheduling in universities was proposed using ABC with Hill Climbing Optimizer (HCO) [59]. HCO is integrated with ABC in the exploration phase to provide better search in the search space.