## 1 Introduction  
Global optimization algorithms are widely used to solve engineering, financial, optimal control problems, as well as problems of clustering, classification, deep machine learning and many others [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. To solve complex applied problems, both deterministic methods of mathematical programming [11; 12; 13] and stochastic metaheuristic optimization algorithms can be used [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. The advantage of the methods of the first group is their guaranteed convergence to the global extremum, and the advantage of the second group is the possibility of obtaining a good-quality solution at acceptable computational costs, even in the absence of convergence guarantees. Among metaheuristic optimization algorithms, various groups are conventionally distinguished: evolutionary methods, swarm intelligence methods, algorithms generated by the laws of biology and physics, multi-start, multi-agent, memetic, and human-based methods. The classification is conditional, since the same algorithm can belong to several groups at once. Four characteristic groups of metaheuristic algorithms can be distinguished, in which heuristics that have proven themselves in solving various optimization problems are coordinated by a higher-level algorithm. Evolutionary methods, in which the search process is associated with the evolution of a solutions set, named a population, include Genetic Algorithms (GA), Self-Organizing Migrating Algorithm (SOMA), Memetic Algorithms (MA), Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMAES), Scatter Search (SS), Artificial Immune Systems (AIS), Variable Mesh Optimization (VMO), Invasive Weed Optimization (IWO), and Cuckoo Search (SC) [3; 4; 15; 16; 17; 18; 20]. The group of Swarm Intelligence algorithms includes Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO), Bat-Inspired Algorithm (BA), Fish School Search (FSS), Cat Swarm Optimization (CSO), Firefly Algorithm (FA), Gray Wolf Optimizer (GWO), While Optimization Algorithm (WOA), Glowworm Swarm Optimization (GSO), Shuffled Frog-Leaping Algorithm (SFLA), Krill Herd (KH), Elephant Herding Optimization (EHO), Lion Pride Optimization Algorithm (LPOA), Spotted Hyena Optimizer (SHO), Spider Monkey Optimization (SMO), Imperialist Competitive Algorithm (ICA), Stochastic Diffusion Search (SDS), Human Group Optimization Algorithm (HGOA), and Perch School Search Algorithm (PSS). In the methods of this group, swarm members (solutions) exchange information during the search process, using information about the absolute leaders and local leaders among neighbors of each solution and their own best positions [2; 15; 16; 22; 23; 24; 25; 26; 27; 28]. The group of physics-based algorithms includes Simulated Annealing (SA), Adaptive Simulated Annealing (ASA), Central Force Optimization (CFO), Big Bang-Big Crunch (BB-BC), Harmony Search (HS), Fireworks Algorithm (FA), Grenade Explosion Method (GEM), Spiral Dynamics Algorithm (SDA), Intelligent Water Drops Algorithm (IWD), Electromagnetism-like Mechanism (EM), and the Gravitational Search Algorithm (GSA) [15; 16; 17; 23; 24; 25; 26; 27; 28]. The group of multi-start-based algorithms includes Greedy Randomized Adaptive Search (GRAS) and Tabu Search (TS) [15; 16; 17]. Among these four groups, bio-inspired optimization algorithms can be distinguished as a part of nature-inspired algorithms [2; 27; 2; 28; 29; 15; 22; 23; 24; 25; 26; 27; 28].