Е-Публикации
Технически университет - София

Abstract: Modern electronic systems increasingly operate in dynamic, nonlinear, and uncertain environments, where classical control methods often fall short in adaptability and optimization performance. Metaheuristic algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Simulated Annealing (SA), offer robust alternatives by enabling global search and solution refinement without requiring precise mathematical models. This paper provides a comprehensive overview of major metaheuristic techniques and their applications in controlling electronic devices, including PID tuning, energy-efficient scheduling, real-time task allocation, and smart sensor calibration. A case study involving GA-based PID parameter optimization demonstrates the practical effectiveness of such approaches in improving control accuracy and system responsiveness. A comparative analysis with traditional methods highlights the strengths and limitations of metaheuristics, while the discussion outlines key challenges, including computational cost and convergence reliability. The paper concludes by exploring hybrid strategies and selfadaptive control systems as promising directions for future research and implementation.

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