## 1 Introduction
For this reason, not only new metaheuristic algorithms are constantly presented in the literature, as mentioned above, but also hybrid metaheuristic algorithms, which are developed by combining several well-known metaheuristic algorithms. These hybrid solutions aim to improve the optimization process for specific problems by mitigating the weaknesses of each algorithm and leveraging their respective strengths [16]. By integrating these algorithms into a new hybrid approach, it becomes possible to adapt and apply them to a broader range of optimization problems [17]. In [18], the authors introduced a taxonomy that categorizes potential hybrid metaheuristic solutions, resulting in the identification of four fundamental approaches to hybridization. One of the four main approaches is the high-level relay hybrid (HRH) solution, in which two metaheuristics are executed in sequence. This HRH solution will be the basis for our new hybrid metaheuristic algorithm. It is important to define the rules by which each metaheuristic is executed in this sequential execution. In this work, hybrid metaheuristics will not have the usual sequential execution of two algorithms, but a special adaptive switch will be introduced within the optimization process between each metaheuristic, according to the results obtained in the last iteration during the optimization. A very important component of any metaheuristic algorithm, including the hybrid algorithm, is the pseudorandom number generator (PRNG), which is used at all stages of randomization within the optimization process. PRNGs are simple deterministic algorithms that generate deterministic sequences of numbers that appear random [19]. Due to the finite number of states in a PRNG, it is inevitable that the generator will eventually return to a previous state and repeat the sequence after a finite number of steps [20]. If the PRNG does not have a large period in which to repeat the random number sequence, the algorithm may end up in a local optimum due to the poor quality of the PRNG. There is a considerable number of scientific papers [7; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32] that introduces a hybrid metaheuristic algorithm based on two or more existing algorithms, but to the best of our knowledge, no hybrid solution mentions the PRNG used for randomization. Therefore, it is assumed that randomization uses random functions implemented by default in the programming language used to implement the metaheuristic algorithm. In [20], it was proven that different PRNGs have a direct impact on the optimization results of a given metaheuristic approach. Therefore, it is very important which PRNG is used in the randomization phase of a single metaheuristic algorithm, as well as a hybrid metaheuristic solution. In our hybrid solution, we will pay special attention to the use of a particular PRNG in the randomization phase, according to the results of testing single PRNGs in [19]. In this way, the probability of premature convergence or staying in the local optimum will be reduced. In this paper, we propose a hybrid nature-inspired metaheuristic solution based on two algorithms: Genetic Algorithm (GA) and African Buffalo Optimization (ABO) [33]. In addition, we introduce the adaptive switch method as part of the hybrid algorithm, which allows dynamic switching between GA and ABO during the optimization process. Unlike the traditional sequential execution, only one algorithm (e.g., ABO) can be executed in several consecutive iterations based on the performance of the single algorithm in the previous iteration. It is worth noting that this hybrid approach can be applied to combine any two-metaheuristic nature-inspired algorithms. In our specific hybrid solution, GA was chosen as a well-established nature-inspired metaheuristic, while ABO represents a more modern nature-inspired metaheuristic.