Skip to main navigation menu Skip to main content Skip to site footer

Electrotechnical and Computer Engineering

Vol. 40 No. 10 (2025): Proceedings of the Faculty of Technical Sciences

COMPARATIVE EXAMINATION AND COMBINATION OF METAHEURISTIC ALGORITHMS IN PATH OPTIMIZATION PROBLEM

  • Игор Мандарић
DOI:
https://doi.org/10.24867/32BE22Mandaric
Submitted
October 19, 2025
Published
2026-01-02

Abstract

This paper addresses three metaheuristic algorithms for the problem of pathfinding and path optimization. The first is the Genetic Algorithm, the second is Ant Colony Optimization, and the third is a hybrid algorithm based on these two, proposed in this work, which demonstrates significant advantages over the other two methods.

References

  1. [1] A. H. Gandomi, X.-S. Yang, S. Talatahari, and A. H. Alavi, “Metaheuristic algorithms in modeling and optimization,” Metaheuristic Appl. Struct. Infrastruct., vol. 1, pp. 1–24, 2013.
  2. [2] J. H. Holland, Adaptation in Natural and Artificial Systems. 1975.
  3. [3] M. Dorigo, “Optimization, Learning and Natural Algorithms,” PhD Thesis Politec. Milano, 1992, Accessed: Dec. 02, 2024. [Online]. Available: https://cir.nii.ac.jp/crid/1573950400977139328
  4. [4] I. Chaari, A. Koubaa, H. Bennaceur, A. Ammar, M. Alajlan, and H. Youssef, “Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments,” Int. J. Adv. Robot. Syst., vol. 14, no. 2, p. 1729881416663663, Mar. 2017, doi: 10.1177/1729881416663663.
  5. [5] O. Mbah and Q. Zeeshan, “Optimizing Path Planning for Smart Vehicles: A Comprehensive Review of Metaheuristic Algorithms,” J. Eng. Manag. Syst. Eng., vol. 2, no. 4, pp. 231–271, Dec. 2023, doi: 10.56578/jemse020405.
  6. [6] J. Luan, Z. Yao, F. Zhao, and X. Song, “A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization,” Math. Comput. Simul., vol. 156, pp. 294–309, Feb. 2019, doi: 10.1016/j.matcom.2018.08.011.
  7. [7] Z. Zukhri and I. V. Paputungan, “A Hybrid Optimization Algorithm based on Genetic Algorithm and Ant Colony Optimization,” Int. J. Artif. Intell. Appl., vol. 4, no. 5, pp. 63–75, Sep. 2013, doi: 10.5121/ijaia.2013.4505.
  8. [8] I. Châari, A. Koubâa, S. Trigui, H. Bennaceur, A. Ammar, and K. Al-Shalfan, “SmartPATH: An Efficient Hybrid ACO-GA Algorithm for Solving the Global Path Planning Problem of Mobile Robots,” Int. J. Adv. Robot. Syst., vol. 11, no. 7, p. 94, Jul. 2014, doi: 10.5772/58543.