FORMING A PORTFOLIO OF IMPORT SUBSTITUTION PROJECTS FOR HIGH-TECH PRODUCTS USING A MODIFIED BEE COLONY ALGORITHM
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Author(s): | Bulygina O. V., Tyukaev D. A., Yashin E. S., |
| Number of journal: | 2(67) | Date: | May 2024 |
| Annotation: | One of the key factors constraining the intensive development of the Russian economy is its high dependence on imports. This problem is especially acute in high-tech industries, where the share of foreign components sometimes exceeds 60-70%. A package of measures of state support for enterprises engaged in the creation of high-tech products has been developed to increase their import independence. However, an extensive list of critically important products that require prompt replacement with Russian analogues makes it advisable to select the most “promising” projects that will be combined into a portfolio to achieve strategic goals. The most difficult process of portfolio formation is determining the optimal list of components (projects and programs) within the given constraints, since it requires the use of special economic and mathematical methods for solving combinatorial optimization tasks. The nonlinearity of the objective function, the high dimensionality of the search space, and information uncertainty significantly complicate the use of traditional deterministic search methods. As an alternative, it is proposed to use metaheuristic algorithms that make it possible to find close to optimal solutions in an acceptable time. To solve the scientific task, it was proposed to use proven methods of swarm intelligence. Among them, the authors chose the artificial bee colony algorithm. The canonical algorithm developed by D. Karaboga is characterized by high flexibility and problem-independence, but is not able to accumulate the “experience” gained in previous iterations and use it to increase convergence. To solve this problem, it was hybridized with a fuzzy clustering algorithm. The division of the entire population into clusters (subpopulations) is carried out to determine objects cluster centers, which will act as starting positions for the search algorithm. | ||
| Keywords: | import substitution, high-tech products, portfolio, project, portfolio optimization and balancing, combinatorial optimization, swarm intelligence, bioinspired algorithm, artificial bee colony algorithm, fuzzy clustering |
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| For citation: | import substitution, high-tech products, portfolio, project, portfolio optimization and balancing, combinatorial optimization, swarm intelligence, bioinspired algorithm, artificial bee colony algorithm, fuzzy clustering |
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