SELF-ADAPTING HYBRID EVOLUTIONARY ALGORITHM FOR TRANSPORT LOGISTICS OPTIMIZATION: DEVELOPMENT, STABILITY ANALYSIS AND PRACTICAL APPLICATION

Sulyukova Larisa Faritovna; Akhmedzhanova Zarrina Iskandarovna; Shamsiev Rasul Zairovich

Authors

Sulyukova Larisa Faritovna Tashkent Institute of Irrigation and Agricultural Mechanization Engineers
Akhmedzhanova Zarrina Iskandarovna Research Institute for the Development of Digital Technologies and Artificial Intelligence
Shamsiev Rasul Zairovich Tashkent State Transport University

Keywords:

Self-adaptation, algorithm stability, hybrid evolutionary algorithm, differential evolution, genetic algorithm, local search, route optimization, transport logistics, agricultural products

Abstract

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.

References

10. Toth P., Vigo D. Vehicle Routing: Problems, Methods, and Applications. Society for Industrial and Applied Mathematics. SIAM, 2014. https://doi.org/10.1137/1.9781611973594

11. Talbi E.-G. Metaheuristics: From Design to Implementation. Wiley, 2009.

12. Eiben A.E., Smith J.E. Introduction to Evolutionary Computing. Springer, 2003. ISBN: 978-3-642-07285-7. https://doi.org/ 10.1007/978-3-662-05094-1.

13. Dib O., Dib M., Caminada A. Computing Multicriteria Shortest Paths in Stochastic Multimodal Networks Using a Memetic Algorithm. 2018. International Journal of Artificial Intelligence Tools 27(07):1860012. https://doi.org/10.1142/S0218213018600126.

14. Dib O., Moalic L., Mainer M.-A., Caminada A. An advanced GA–VNS combination for multicriteria route planning in public transit networks. Expert Systems with Applications Volume 72, 15 April 2017, Pages 67-82 https://doi.org/10.1016/j.eswa.2016.12.009

15. https://www.sciencedirect.com/science/article/abs/pii/S0957417416306820

16. Cao E., M. Lai, and K. Nie, “A Differential Evolution & Genetic Algorithm for Vehicle Routing Problem with Simultaneous Delivery and Pick-up and Time Windows” IFAC Proceedings Volumes, vol. 41, no. 2, pp. 10576–10581, 2008. https://doi.org/10.3182/20080706-5-KR-1001.01791.

17. Cordeau J.-F., Laporte G., Ropke S. Recent Models and Algorithms for One-to-One Pickup and Delivery Problems. Operations Research/Computer Science Interfaces, vol 43, pp. 327-357, Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77778-8_15.

18. Kachitvichyanukul V. Comparison of three evolutionary algorithms: GA, PSO, and DE. 2012. Industrial Engineering & Management Systems 12(3):215-223. https://doi.org/10.7232/iems.2012.11.3.215