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The paper considers the application of swarm intelligence technology to build agent-based simulation models. As an example, a minimal model is constructed illustrating the influence of information influences on the rules of behavior of agents in the simplest model of competition between two populations, whose agents perform the simplest task of transferring a resource from a mobile source to their territory. The algorithm for the movement of agents in the model space is implemented on the basis of the classical particle swarm algorithm. Agents have a life cycle, that is, the processes of birth and death are taken into account. The model takes into account information processes that determine the target functions of the behavior of newly appeared agents. These processes (training and poaching) are determined by information influences from populations. Under certain conditions, a third population arises in the agent system. Agents of such a population informatively influence agents of other populations in a certain radius around themselves, changing.

As a result of the conducted simulation experiments, it was shown that the following final states are realized in the system: displacement of a new population by others, coexistence of a new population and other populations and the absence of such a population. It has been shown that with an increase in the radius of influence of agents, the population with changed rules of behavior displaces all others. It is also shown that in the case of a hard-to-access resource, the strategy of luring agents of a competing population is more profitable.

Epidemics severely destabilize economies by reducing productivity, weakening consumer spending, and overwhelming public infrastructure, often culminating in economic recessions. The COVID-19 pandemic underscored the critical role of nonpharmaceutical interventions, such as lockdowns, in containing infectious disease transmission. This study investigates how the progression of epidemics and the implementation of lockdown policies shape the economic well-being of populations. By integrating compartmental ordinary differential equation (ODE) models, the research analyzes the interplay between epidemic dynamics and economic outcomes, particularly focusing on how varying lockdown intensities influence both disease spread and population wealth. Findings reveal that epidemics inflict significant economic damage, but timely and stringent lockdowns can mitigate healthcare system overload by sharply reducing infection peaks and delaying the epidemic’s trajectory. However, carefully timed lockdown relaxation is equally vital to prevent resurgent outbreaks. The study identifies key epidemiological thresholds—such as transmission rates, recovery rates, and the basic reproduction number $(\mathfrak{R}0)$ — that determine the effectiveness of lockdowns. Analytically, it pinpoints the optimal proportion of isolated individuals required to minimize total infections in scenarios where permanent immunity is assumed. Economically, the analysis quantifies lockdown impacts by tracking population wealth, demonstrating that economic outcomes depend heavily on the fraction of isolated individuals who remain economically productive. Higher proportions of productive individuals during lockdowns correlate with better wealth retention, even under fixed epidemic conditions. These insights equip policymakers with actionable frameworks to design balanced lockdown strategies that curb disease spread while safeguarding economic stability during future health crises.

This study proposes and analyzes a discrete-time mathematical model of population dynamics with seasonal reproduction, taking into account the density-dependent regulation and sex structure. In the model, population birth rate depends on the number of females, while density is regulated through juvenile survival, which decreases exponentially with increasing total population size. Analytical and numerical investigations of the model demonstrate that when more than half of both females and males survive, the population exhibits stable dynamics even at relatively high birth rates. Oscillations arise when the limitation of female survival exceeds that of male survival. Increasing the intensity of male survival limitation can stabilize population dynamics, an effect particularly evident when the proportion of female offspring is low. Depending on parameter values, the model exhibits stable, periodic, or irregular dynamics, including multistability, where changes in current population size driven by external factors can shift the system between coexisting dynamic modes. To apply the model to real populations, we propose an approach for estimating demographic parameters based on total abundance data. The key idea is to reduce the two-component discrete model with sex structure to a delay equation dependent only on total population size. In this formulation, the initial sex structure is expressed through total abundance and depends on demographic parameters. The resulting one-dimensional equation was applied to describe and estimate demographic characteristics of ungulate populations in the Jewish Autonomous Region. The delay equation provides a good fit to the observed dynamics of ungulate populations, capturing long-term trends in abundance. Point estimates of parameters fall within biologically meaningful ranges and produce population dynamics consistent with field observations. For moose, roe deer, and musk deer, the model suggests predominantly stable dynamics, while annual fluctuations are primarily driven by external factors and represent deviations from equilibrium. Overall, these estimates enable the analysis of structured population dynamics alongside short-term forecasting based on total abundance data.

The principle of minimal differentiation, based on the Hotelling model, is well known in the economy. It is applicable to horizontal differentiated goods of almost any nature. The Hotelling approach to modeling competition of oligopolies corresponds to a modern description of monopolistic competition with increasing returns to scale and imperfect competition. We develop a modification of the Hotelling model that endows a firm with a non-market advantage, which is introduced alike the valence advantage known in problems of political economy. The nonmarket (valence) advantage can be interpreted as advertisement (brand awareness of firms). Problem statement. Consider two firms competing with prices and location. Homogeneous consumers vary with its location on a segment. They minimize their costs, which additively includes the price of the product and the distance from them to the product. The utility function is linear with respect to the price and quadratic with respect to the distance. It is also expected that one of the firms (for certainty, firm № 1) has a market advantage d. The consumers are assumed to take into account the sum of the distance to the product and the market advantage of firm 1. Thus, the strategy of the firms and the consumers depend on two parameters: the unit t of the transport costs and the non-market advantage d. I explore characteristics of the equilibrium in the model as a function of the non-market advantage for different fixed t. The aim of the research is to assess the impact of the non-market advantage on the equlibrium. We prove that the Nash equilibrium exists and it is unique under additive consumers' preferences de-pending on the square of the distance between consumers and firms. This equilibrium is ‘richer’ than that in the original Hotelling model. In particular, non-market advantage can be excessive and inefficient to use.

The Lipschitz continuous property has been used for a long time to solve the global optimization problem and continues to be used. Here we can mention the work of Piyavskii, Yevtushenko, Strongin, Shubert, Sergeyev, Kvasov and others. Most papers assume a priori knowledge of the Lipschitz constant, but the derivation of this constant is a separate problem. Further still, we must prove that an objective function is really Lipschitz, and it is a complicated problem too. In the case where the Lipschitz continuity is established, Strongin proposed an algorithm for global optimization of a satisfying Lipschitz condition on a compact interval function without any a priori knowledge of the Lipschitz estimate. The algorithm not only finds a global extremum, but it determines the Lipschitz estimate too. It is known that every function that satisfies the Lipchitz condition on a compact convex set is uniformly continuous, but the reverse is not always true. However, there exist models (Arutyunova, Dulliev, Zabotin) whose study requires a minimization of the continuous but definitely not Lipschitz function. One of the algorithms for solving such a problem was proposed by R. J. Vanderbei. In his work he introduced some generalization of the Lipchitz property named $\varepsilon$-Lipchitz and proved that a function defined on a compact convex set is uniformly continuous if and only if it satisfies the $\varepsilon$-Lipchitz condition. The above-mentioned property allowed him to extend Piyavskii’s method. However, Vanderbei assumed that for a given value of $\varepsilon$ it is possible to obtain an associate Lipschitz $\varepsilon$-constant, which is a very difficult problem. Thus, there is a need to construct, for a function continuous on a compact convex domain, a global optimization algorithm which works in some way like Strongin’s algorithm, i.e., without any a priori knowledge of the Lipschitz $\varepsilon$-constant. In this paper we propose an extension of Strongin’s global optimization algorithm to a function continuous on a compact interval using the $\varepsilon$-Lipchitz conception, prove its convergence and solve some numerical examples using the software that implements the developed method.

The paper deals with a prey-predator model, which describes the spatiotemporal dynamics of plankton community and the nutrients. The system is described by reaction-diffusion-advection equations in a onedimensional vertical column of water in the surface layer. Advective term of the predator equation represents the vertical movements of zooplankton with velocity, which is assumed to be proportional to the gradient of phytoplankton density. This study aimed to determine the conditions under which these movements (taxis) lead to the spatially heterogeneous structures generated by the system. Assuming diffusion coefficients of all model components to be equal the instability of the system in the vicinity of stationary homogeneous state with respect to small inhomogeneous perturbations is analyzed.

Necessary conditions for the flow-induced instability were obtained through linear stability analysis. Depending on the local kinetics parameters, increasing the taxis rate leads to Turing or wave instability. This fact is in good agreement with conditions for the emergence of spatial and spatiotemporal patterns in a minimal phytoplankton–zooplankton model after flow-induced instabilities derived by other authors. This mechanism of generating patchiness is more general than the Turing mechanism, which depends on strong conditions on the diffusion coefficients.

While the taxis exceeding a certain critical value, the wave number corresponding to the fastest growing mode remains unchanged. This value determines the type of spatial structure. In support of obtained results, the paper presents the spatiotemporal dynamics of the model components demonstrating Turing-type pattern and standing wave pattern.

In this article we consider min-min type of problems or minimization by two groups of variables. In some way it is similar to classic min-max saddle point problem. Although, saddle point problems are usually more difficult in some way. Min-min problems may occur in case if some groups of variables in convex optimization have different dimensions or if these groups have different domains. Such problem structure gives us an ability to split the main task to subproblems, and allows to tackle it with mixed oracles. However existing articles on this topic cover only zeroth and first order oracles, in our work we consider high-order tensor methods to solve inner problem and fast gradient method to solve outer problem.

We assume, that outer problem is constrained to some convex compact set, and for the inner problem we consider both unconstrained case and being constrained to some convex compact set. By definition, tensor methods use high-order derivatives, so the time per single iteration of the method depends a lot on the dimensionality of the problem it solves. Therefore, we suggest, that the dimension of the inner problem variable is not greater than 1000. Additionally, we need some specific assumptions to be able to use mixed oracles. Firstly, we assume, that the objective is convex in both groups of variables and its gradient by both variables is Lipschitz continuous. Secondly, we assume the inner problem is strongly convex and its gradient is Lipschitz continuous. Also, since we are going to use tensor methods for inner problem, we need it to be p-th order Lipschitz continuous ($p > 1$). Finally, we assume strong convexity of the outer problem to be able to use fast gradient method for strongly convex functions.

We need to emphasize, that we use superfast tensor method to tackle inner subproblem in unconstrained case. And when we solve inner problem on compact set, we use accelerated high-order composite proximal method.

Additionally, in the end of the article we compare the theoretical complexity of obtained methods with regular gradient method, which solves the mentioned problem as regular convex optimization problem and doesn’t take into account its structure (Remarks 1 and 2).

The work is devoted to the study of subgradient methods with different variations of the Polyak stepsize for minimization functions from the class of weakly convex and relatively weakly convex functions that have the corresponding analogue of a sharp minimum. It turns out that, under certain assumptions about the starting point, such an approach can make it possible to justify the convergence of the subgradient method with the speed of a geometric progression. For the subgradient method with the Polyak stepsize, a refined estimate for the rate of convergence is proved for minimization problems for weakly convex functions with a sharp minimum. The feature of this estimate is an additional consideration of the decrease of the distance from the current point of the method to the set of solutions with the increase in the number of iterations. The results of numerical experiments for the phase reconstruction problem (which is weakly convex and has a sharp minimum) are presented, demonstrating the effectiveness of the proposed approach to estimating the rate of convergence compared to the known one. Next, we propose a variation of the subgradient method with switching over productive and non-productive steps for weakly convex problems with inequality constraints and obtain the corresponding analog of the result on convergence with the rate of geometric progression. For the subgradient method with the corresponding variation of the Polyak stepsize on the class of relatively Lipschitz and relatively weakly convex functions with a relative analogue of a sharp minimum, it was obtained conditions that guarantee the convergence of such a subgradient method at the rate of a geometric progression. Finally, a theoretical result is obtained that describes the influence of the error of the information about the (sub)gradient available by the subgradient method and the objective function on the estimation of the quality of the obtained approximate solution. It is proved that for a sufficiently small error $\delta > 0$, one can guarantee that the accuracy of the solution is comparable to $\delta$.

This article presents a model of a fully automated warehouse with deep storage racks designed for boxed goods storage. The study focuses on optimizing warehouse operations through discrete multiagent simulation of shuttle movements for pallet loading and unloading tasks. The authors investigate various product placement strategies, including the Nearest Channel Positioning Algorithm (NCPA), Most Empty Channel Group Placement (MECGP), andMost Filled Channel Group Placement (MFCGP), while analyzing optimal routing schemes for the given warehouse topology.

A key contribution is determining the optimal number of shuttles to maximize warehouse throughput. Simulation results demonstrate that increasing the number of robots beyond 15 does not significantly improve efficiency due to increased route collisions. The study also examines 24-hour warehouse occupancy dynamics, revealing optimal storage utilization levels.

The developed model enables performance evaluation and optimization of task distribution among robots to minimize order processing time. Future research directions include implementing machine learning techniques to further enhance warehouse management systems.

This article presents an integrated dynamic model of eco-economic system of the Republic of Armenia (RA). This model is constructed using system dynamics methods, which allow to consider the major feedback related to key characteristics of eco-economic system. Such model is a two-objective optimization problem where as target functions the level of air pollution and gross profit of national economy are considered. The air pollution is minimized due to modernization of stationary and mobile sources of pollution at simultaneous maximization of gross profit of national economy. At the same time considered eco-economic system is characterized by the presence of internal constraints that must be accounted at acceptance of strategic decisions. As a result, we proposed a systematic approach that allows forming sustainable solutions for the development of the production sector of RA while minimizing the impact on the environment. With the proposed approach, in particular, we can form a plan for optimal enterprise modernization and predict long-term dynamics of harmful emissions into the atmosphere.