Pandemics in modern history are not uncommon. In 2019, the global health system was tested by the SARS-Covid-19 pandemic. The modern public has reacted with the introduction of a mask regime, the introduction of lock-down, the abrupt opening of quarantine zones and departments of medical centers. Considering the increased cargo and passenger traffic, restrictions bring great difficulties. For medical centers, it is expected that it will be possible to quickly (up to two weeks) create quarantine zones and open additional quarantine departments. These activities involve hiring new staff, purchasing supplies, training and organizing staff and residents (or patients). With this approach, situational management is often used, when the decision to open additional branches is given when the existing ones are filled.
To simplify management decision-making, it is necessary to predict the situation. Existing forecasting systems are usually based on statistical information and use machine learning methods. These systems require a sufficiently large training sample (i.e., the accuracy depends on the time of collection of statistical information and its accuracy).
To describe the process of changing the number of healthy, sick, recovered people, there are systems of differential equations, for example, SIR, SEIR, SIRVD, etc. Where the number of people belonging to the categories is considered: Susceptible, Exposed, Infected, Recovered, Vaccinated, Dead. To tune the system, it is necessary to calculate the weight coefficients of the system of differential equations.
Simulation and analytical models can more accurately describe some of the processes taking place during a pandemic. These models are not based on statistical data, but on the description of the process of changing the state of the system over time. In such models, it is possible to take into account the methods of infection transmission, quarantine rules, various levels of quarantine and methods of testing the population, vaccination processes, transport, logistics processes, and others. The main problem of these systems is the need to check the adequacy of the model. Due to the large number of stochastic factors, a large number of accurate models are inadequate. For simulation and analytical models, the parameters can be varied and be both quantitative and qualitative. Calculation of model parameters, allowing building an adequate model, is a labor-intensive task. The use of expert assessments also requires a thorough check of the processes in the simulation model. But the availability of statistical data allows not only assessing the adequacy of the processes of the functioning of the model, but also the parameters of this model.
At present, it has become possible to transfer many optimization, computational tasks from humans to computers. The user of such a system describes sets of parameters, and the computing system selects the optimal parameters by enumerating them. Most often, enumeration of parameters is carried out by brute force methods, since the computer system solves not an optimization problem, but a calculation one.
The paper proposes to develop software that could rearrange parameter sets so that rational sets are calculated earlier than if they were considered sequentially. The input data of such a system are sets of parameters and, in the process of operation, the values of criteria for a particular set of parameters. Recent results such as [1-4] demonstrate that the problem of optimizing hyperparameters in large and multilayer models is a direct obstacle to scientific progress. There are similar systems, for example, the Bayesian optimizer (IBM Bayesian Optimization Accelerator (BOA)). In world practice, studies of the Bayesian optimizer are widespread [5-13]. IBM has taken an artificial intelligence approach based on Bayesian optimization, which builds and optimizes the model in real time to predict the most «promising» points that are calculated by existing tools. However, BOA-based solutions are expensive, require a separate computing cluster, and use metaheuristics to generate multiple hypotheses. These metaheuristics are commercially closed and are not subject to analysis. These features often become disadvantages of the Bayesian optimizer.
The paper considers the application of the developed modification of the ant colony method for directed enumeration of hyperparameters. The algorithm of the ant colony method, developed for finding the traveling salesman path [14-16], can be easily modified for parametric problems [15-21]. In the presented works, the task of the ant colony method is to find rational solutions, while the majority of ants (agents) must move along the same path. At the same time, modifications that allow ants to find new, non-repeating solutions at each iteration were not considered by the scientific community. This approach is necessary when the task.
Methods and techniques
The operation of the ant colony method requires a graph structure, along the arcs of which agents (ants) move. For parametric optimization, the graph familiar to the traveling salesman problem is, in fact, a set of linked lists [21-29]. Each list defines a set of parameter values and may be referred to as a layer. The algorithm selects one vertex in each layer (i.e. for each parameter), i.e. parameter value. To take into account the already considered vertices, it is supposed to enter the paths of agents in the Hash table. In general terms, the modified ant colony method for directed enumeration of hyperparameters is shown in Figure 1. The block «Calculation of criteria values for a certain set of parameters» is supposed to be performed based on the operation of a simulation or analytical model that takes a vector of parameter values as input, and, as output variables, gives the value of the optimality criteria for the set of parameters. This approach allows testing the operation of the modified ant colony method independently of a complex analytical or simulation model, for example, on a simple simulation model.
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