## 1 Introduction  
Many real-world problems can be formulated as optimization problems, which are characterized by different properties such as, for example, many local optima, non-separability, asymmetricity, etc. These problems arise from various scientific fields, such as engineering and related areas. For solving such kinds of problems, researchers have presented different methods over recent years, and heuristic optimization methods and their modifications are among them [1, 2]. Random search-based and nature-inspired algorithms are faster and more efficient than traditional methods ([PERSON]'s method, bisection method, Hooke-Jeeves method, etc.) while solving high-dimensional complex multi-modal optimization problems, for example [3]. However, they also have difficulties in keeping the balance between exploration (the procedure of finding completely new areas of the search space) and exploitation (the procedure of finding the regions of a search space close to previously visited points) when solving these problems [4, 5, 6]. Biology-inspired (population-based) algorithms such as Particle Swarm Optimization (PSO) [7], Ant Colony Optimization (ACO) [8], the Artificial Bee Colony (ABC) [9], the Whale Optimization Algorithm (WOA) [10], the Grey Wolf Optimizer (GWO) [11], the Artificial Algae Algorithm (AAA) [12], Meth-Flame Optimization (MPO) [13] and others are among the most popular and frequently used heuristic optimization methods. These algorithms imitate the behavior of a group of animals (individual or social) or some of their features. Thus, the process of optimization consists of generating a set of random solutions (called individuals in other words) and leading them to the optimal solution for a given problem. Biology-inspired algorithms have found a variety of applications for real-world problems from different areas due to their high efficiency, for example [14; 15; 16]. These algorithms gained popularity among research due to the fact that they can be used for solving various optimization problems regardless of the objective function's features. Nevertheless, according to the No Free Lunch (NFL) theorem there is no universal method for solving all optimization problems [17]. Therefore, researchers propose new optimization algorithms to increase the efficiency of the currently existing algorithms for solving a wider range of optimization problems. One way to modify currently existing techniques consists of developing collective meta-heuristics, which use the advantages of several techniques at the same time and, therefore, are more efficient [18; 19; 20]. In [21] the meta-heuristic approach COBRA (Co-Operation of Biology-Related Algorithms) based on parallel functioning of several populations was proposed for solving unconstrained real-valued optimization problems. Its main idea can be described as the simultaneous work of several biology-inspired algorithms with similar schemes, which compete and cooperate with each other. The original version of the COBRA consisted of six popular biology-inspired component algorithms, namely the Particle Swarm Optimization Algorithm (PSO) [7], the Cuckoo Search Algorithm (CSA) [22], the Firefly Algorithm (FFA) [23], the Bat Algorithm (BA) [24], the Fish School Search Algorithm (FSS) [25] and, finally, the Wolf Pack Search Algorithm (WPS) [26]. However, various other heuristics can be used as component algorithms (for example, the ones mentioned above) for COBRA as well as previously conducted experiments demonstrating that even the already chosen bio-inspired algorithms can be combined differently [27]. Later, the fuzzy logic-based controllers [28] were proposed for the automated selection of biology-inspired algorithms to be included in the ensemble from a predefined set, and the number of individuals in each population [29]. The idea of using fuzzy controllers for parameter adaptation of the heuristic was previously explored by researchers, for instance in [30; 31], and their usefulness was established.