(untitled) (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
This paper presents a self-adaptive hybrid evolutionary algorithm for solving transport route optimization problems in agricultural logistics. The developed algorithm combines differential evolution (DE), a genetic algorithm (GA), and variable-environment local search (VNS) with a self-adaptive parameter optimization (SaDE) mechanism. SaDE automatically adjusts key algorithm parameters based on their success in previous generations, ensuring an adaptive balance between global search and local exploitation. A comprehensive analysis of the algorithm’s stability was conducted on problems of varying dimensions (from 50 to 200 orders). Experimental results demonstrate a 15-18% reduction in transportation costs compared to baseline heuristics with a variation coefficient of only 1.40%, which is 3.5 times better than a classical genetic algorithm. The high stability and reproducibility of the results confirm the algorithm’s readiness for integration into real-world transport logistics management systems.