## 1 Introduction
Evolutionary algorithms such as Genetic Algorithms (GA) [14; 15] and Evolution Strategies [16]; Ant Colony Optimization (ACO) [17], Simulated Annealing (SA) [18], Firefly Algorithm (FA) [19], Artificial Bee Colony (ABC) optimization [20], Cuckoo Search Algorithm (CS) [21], etc., are, among many others, some of the examples of classical metaheuristics. Some of the population-based algorithms such as GA, FA and CS could be considered both as biology-based and swarm-based. Some of the most powerful nature-inspired metaheuristics have been developed and tested for solving different optimization problems. A comprehensive review of metaheuristic algorithms is presented in [22]. Since optimization is a process of making things as efficient as possible, researchers continuously seek new and modified metaheuristic methods that outperform the existing ones. The application of metaheuristic techniques for solving numerical optimization problems is receiving increasing attention. A new performance assessment method is discussed in [23]. It considers the influence of the control parameters on the metaheuristic algorithms. The method is demonstrated for GA. As a result, a better solution than the best reported so far in the literature is detected. Recently, new performance metrics of metaheuristic optimization methods have been proposed in [24]. The presented results show that the discussed metrics have a good distinctive performance for different algorithms. The best-found algorithm is Differential Evolution (DE). In [25], an NP-hard problem is solved based on multi-objective Tabu Search (TS), multi-objective variable neighborhood search, and multi-objective particle mass optimization. The presented results show that the performance of multi-objective TS is better than that of other proposed metaheuristic algorithms. Real-life problems generally exhibit nonlinear constraints and dynamic components. GAs are well-known metaheuristics extremely applicable to problems with such characteristics. They have been well employed in many fields with still growing recognition [26]. GAs are often used instead of traditional optimization methods. Authors of [27] proposed a hybrid where the crossover and mutation operators of GA are integrated with the Teaching-Learning-Based Optimization and Particle Swarm Optimization (PSO) algorithms. The elitist strategy is utilized to boost evolutionary efficiency. A hybrid GA-PSO is proposed in [28] and compared with CS, PSO, GA, and Simulated Annealing. The results of this research indicate that PSO and GA-PSO produce optimal values for all considered objective functions. So far, their effectiveness and robustness have been demonstrated in the mathematical modelling of fermentation processes. In [29], GA and the Artificial Bee Colony algorithm are applied to cultivation process modelling. A new approach for simultaneous parameter tuning of the metaheuristics is proposed. An optimization of wheat germ fermentation conditions using an artificial neural network combined with GA is discussed in [30]. Based on the optimized scheme, a 117% improvement is achieved compared to that of the control group. Authors in [31] propose GA for optimizing the productivity of the yeast fermentation process. The proposed GA obtains a higher yield production than the conventional open-loop system. A comparison of 8 modifications of GA (simple genetic algorithms and multi-population ones) is presented in [32] for parameter identification of fed-batch cultivation of _S. cerevisiae_. GA with the sequence of mutation, crossover, and selection operators is significantly faster than the other modifications. Algorithms such as GA can be very useful, but they still have some drawbacks when dealing with multimodal optimization problems [33]. Some results show that FA outperforms GA and is a powerful algorithm to solve even NP-hard problems [19; 34; 35]. In [36], an improved FA is utilized to solve the multi-depot vehicle routing problem with time windows.