METAHEURISTIC FRAMEWORKS FOR PARAMETER ESTIMATION IN APPROXIMATION MODELS
DOI:
https://doi.org/10.15588/1607-3274-2025-4-8Keywords:
probabilistic optimization frameworks, hybrid metaheuristic techniques, adaptive search algorithms, annealingbased simulation, computational parameter estimation, parametric identification, economics approximation modelAbstract
Context. To enhance the performance of numerical optimization techniques, hybrid approaches integrating probabilistic modeling algorithms with annealing simulation have been introduced. These include Bayesian optimization, Markov-based strategies, and extended compact genetic algorithms, each augmented by annealing mechanisms. Such methods enable more precise search trajectories without requiring fitness function transformation, owing to their ability to explore the global search space in early iterations and refine the directionality of search in later stages.
Objective. The research aims to improve the effectiveness of parameter identification within approximation models of financial indicators by applying metaheuristic algorithms that incorporate probabilistic modeling and annealing-based simulation in intelligent computing systems.
Method. This study employs metaheuristic techniques grounded in probabilistic modeling and annealing-based simulation to enhance the accuracy and efficiency of parameter estimation within economic indicator approximation frameworks. Specifically, it introduces three hybrid strategies: Bayesian-based optimization integrated with annealing simulation, Markov-driven optimization enhanced by annealing, and an extended compact genetic algorithm coupled with annealing mechanisms. These methods enhance the accuracy of the search process by exploring the entire search space in initial iterations and refining the search direction in final iterations. The Bayesian optimization method employs a Bayesian network for structured search and solution refinement. The Markov optimization method integrates Gibbs quantization within a Markov network to improve search precision. The extended compact genetic algorithm utilizes limit distribution models to generate optimal solutions. These methods eliminate the need for fitness function transformation, optimizing computational efficiency. The proposed techniques expand the application of metaheuristics in intelligent economic computer systems.
Results. The implemented optimization strategies significantly enhanced the precision of parameter estimation within intelligent financial computing frameworks. The combination of probabilistic models and annealing simulation enhanced search efficiency without requiring fitness function transformation.
Conclusions. The proposed method expands the application of metaheuristics in economic modeling, increasing computational effectiveness. Further research should explore their implementation across diverse artificial intelligence problems.
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Copyright (c) 2025 O. O. Grygor, E. E. Fedorov, M. M. Leshchenko, K. S. Rudakov, T. A. Sakhno
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