Application of the ant colony method for solving parametric problems in the aerospace industry


Аuthors

Titov Y. 1*, Sudakov V. 2**

1. Moscow Aviation Institute (National Research University), 4, Volokolamskoe shosse, Moscow, А-80, GSP-3, 125993, Russia
2. Keldysh Institute of Applied Mathematics, KIAM, Moscow, Russia

*e-mail: kalengul@mail.ru
**e-mail: sudakov@ws-dss.com

Abstract

This paper presents a new direction in the development of the ant colony method (ACO), which consists in dividing its work into several layers. Each layer is responsible for certain aspects of the problem solution, which allows for a more detailed and accurate consideration of the influence of various factors on the optimization results. During the algorithm operation, the values from these layers are convolved into one common value, with subsequent normalization for use in the probabilistic formula, which contributes to a deeper analysis and normalization of the data. An important aspect of the work is the new probabilistic formula proposed for the ant colony method when solving parametric problems. This formula allows the algorithm to more accurately model the behavior of ants depending on the changing conditions of the problem. Due to this, the algorithm becomes more resistant to changes in external factors and can dynamically adapt to new data. This property is especially relevant for real-world applications in the aerospace industry.
Particular attention is paid to modifications of the ant colony method developed for solving problems on graphs specific to the aerospace industry. The unique characteristics of this domain, such as limited time windows and different types of vehicles, require adaptation of traditional algorithms. The proposed modifications allow taking these features into account, which leads to more efficient routing and resource allocation.
Using the proposed modifications of the ant colony method, clustering of passenger air routes between cities was carried out. In addition, the paper describes the successful application of the modified ant colony method to solving parametric problems of assigning employees to jobs and calculating the values of the model hyperparameters.
Finally, it is worth noting that the proposed modifications of the ant colony method have demonstrated their effectiveness not only in transport logistics and employee assignment problems, but also in the context of multi-criteria optimization. The ability to work on SIMD processors opens up new horizons for parallel data processing and increases the speed of algorithm execution. This makes the method especially attractive for solving complex problems in real time, which is an important requirement for modern aerospace applications. Thus, the paper represents a significant contribution to the development of optimization methods, proposing new approaches and solutions to current problems in the aerospace industry.

Keywords:

ant colony method, metaheuristic optimization, parametric problem, computing cluster, resource collection problem, assignment problem

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