## 1 Introduction  
Global optimization methods are widely used in engineering calculations when designing complex technical systems, problems of information processing, decision making and optimal control, machine learning, etc. [1, 2, 3, 4, 5, 6, 7]. As a rule, the application of the necessary optimality conditions for finding the optimal open-loop control of discrete deterministic systems is associated with the solution of a boundary value problem for a system of difference equations, the computational complexity of which greatly increases with the state vector dimension. The use of quasi-gradient optimization methods often leads to obtaining locally optimal solutions. The use of direct optimization methods, such as the Luus-Jaacola method, makes it possible to obtain a result with the desired accuracy with an acceptable number of runs of the algorithm. Objective functions can be multi-extremal, nondifferentiable, difficult to calculate, or generally described by a black box model. Therefore, the application of necessary and sufficient optimality conditions, as well as quasi-gradient methods of the first and second orders, could be difficult. Among the zero-order methods, in which the search direction is determined only by information about the value of the objective function, two groups can be distinguished: stochastic metaheuristic algorithms [8, 9, 10, 11, 12, 13, 14, 15, 16] and deterministic methods of mathematical programming [17, 18, 19]. The former have gained popularity among applied mathematicians and engineers because they use various original heuristics driven by higher-level algorithms. Proof of convergence for these algorithms is usually complex and, thus, hard to provide, but they are able to obtain solutions of sufficiently good quality in an acceptable time. The second group of methods is popular among mathematicians, since, under certain conditions, it can guarantee convergence to the global extremum. In the first group, it is conditionally possible to single out four well-known areas of research. The first one includes evolutionary methods, in which the search process is associated with the evolution of a solutions population. These include Genetic Algorithms, Differential Evolution, Artificial Immune Systems, Covariance Matrix Adaptation Evolution Strategy, Scatter Search, Variable Mesh Optimization, Memetic Algorithms, Weed Colonization, Cuckoo Search, Self-Organizing Migrating Algorithms, etc. The second area is represented by Swarm Intelligence algorithms: Particle Swarm Optimization, Artificial Bee Colony, Ant Colony Optimization, Bat-Inspired Algorithm, Cat Swarm Optimization, Firefly Algorithm, Grey Wolf Optimizer, Whale Optimization Algorithm, Bacterial Foraging Optimization, Fish School Search, Glowworm Swarm Optimization, Shuffled Prog-Leaping Algorithm, Krill Herd, Imperialist Competitive Algorithm, Stochastic Diffusion Search, Human Group Optimization Algorithm, etc. In this area, new original algorithms are being created and considered [20]. In the methods of this group, members of the populations exchange information during the search process and use information about the whole population leaders, local leaders among neighbors, and their own best results. The third area is represented by physics-based algorithms: Central Force Optimization, Simulated Annealing, Adaptive Simulated Annealing, Harmony Search, Big Bang-Big Crunch, Spiral Dynamics Algorithm, Fireworks Algorithm, Grenade Explosion Method, Intelligent Water Drops Algorithm, Electromagnetism-like Mechanism, etc. The fourth area is represented by multistart-based algorithms: Greedy Randomized Adaptive Search, Tabu Search, etc. In the four listed areas, a group of bio-inspired algorithms can be distinguished due to their nature-inspired interpretations [2; 16]. This article is devoted to the development of the swarm intelligence group of methods based on observing the hunting of a school of river perches and hybridization mechanisms.