Review Article

Tool Path Optimization A Systematic Literature Review On Metaheuristic Algorithms

Number: 1 October 6, 2026
Necat Ozan Gençay , Yusuf Soner Demirel , Kürşat Tanrıver *, Mustafa Ay
TR EN

Tool Path Optimization A Systematic Literature Review On Metaheuristic Algorithms

Abstract

This study aims to systematically review the post-2021 literature on tool path optimization for multi-hole drilling processes, focusing on the metaheuristic algorithms most frequently applied to this combinatorial problem — namely Ant Colony Optimization (ACO), the Bees Algorithm, the Genetic Algorithm (GA), and the Grey Wolf Optimizer (GWO) — and to comparatively evaluate their relative performance, parameter-tuning burden, and hybridization trends across different problem scales. A systematic search was conducted through Google Scholar using keyword combinations related to tool path optimization, drilling sequence, and the corresponding algorithm families; studies published from 2021 onward that directly addressed hole or tool path optimization, or related Traveling Salesman Problem formulations, were included following a PRISMA-based selection process. A total of 41 application studies met the inclusion criteria and were classified according to the algorithm family employed, then synthesized within a comparative framework alongside seven founding papers used to define the underlying algorithms. Approximately 46% of the reviewed studies employed the Bees Algorithm or ACO, reflecting their maturity across both small-scale benchmarks and large-scale industrial scenarios, while the Grey Wolf Optimizer emerged as a rapidly growing alternative in energy-focused and laser micro-hole applications. No single algorithm consistently outperformed others across all problem scales: Simulated Annealing and the Bees Algorithm performed strongly on small-to-medium regular problems, whereas Ant Colony Optimization and Dhouib-Matrix approaches proved more robust on large, irregular layouts, and hybridization of exploration and exploitation mechanisms emerged as the dominant research direction. Tool path optimization in multi-hole drilling remains an active, rapidly evolving field in which metaheuristic algorithms deliver measurable manufacturing efficiency gains; algorithm selection should be scale-dependent, and future research should prioritize physical validation and underexplored methods such as Cuckoo Search, the Shuffled Frog-Leaping Algorithm, and Reinforcement Learning.

Keywords

Multi-Hole Drilling, Tool Path Optimization, Metaheuristics, Ant Colony Optimization, Bees Algorithm

References

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APA
Gençay, N. O., Demirel, Y. S., Tanrıver, K., & Ay, M. (2026). Tool Path Optimization A Systematic Literature Review On Metaheuristic Algorithms. Journal of Science, Technology and Engineering Research, 1. https://doi.org/10.53525/jster.2013127
AMA
1.Gençay NO, Demirel YS, Tanrıver K, Ay M. Tool Path Optimization A Systematic Literature Review On Metaheuristic Algorithms. Journal of Science, Technology and Engineering Research. 2026;(1). doi:10.53525/jster.2013127
Chicago
Gençay, Necat Ozan, Yusuf Soner Demirel, Kürşat Tanrıver, and Mustafa Ay. 2026. “Tool Path Optimization A Systematic Literature Review On Metaheuristic Algorithms”. Journal of Science, Technology and Engineering Research, no. 1. https://doi.org/10.53525/jster.2013127.
EndNote
Gençay NO, Demirel YS, Tanrıver K, Ay M (October 1, 2026) Tool Path Optimization A Systematic Literature Review On Metaheuristic Algorithms. Journal of Science, Technology and Engineering Research 1
IEEE
[1]N. O. Gençay, Y. S. Demirel, K. Tanrıver, and M. Ay, “Tool Path Optimization A Systematic Literature Review On Metaheuristic Algorithms”, Journal of Science, Technology and Engineering Research, no. 1, Oct. 2026, doi: 10.53525/jster.2013127.
ISNAD
Gençay, Necat Ozan - Demirel, Yusuf Soner - Tanrıver, Kürşat - Ay, Mustafa. “Tool Path Optimization A Systematic Literature Review On Metaheuristic Algorithms”. Journal of Science, Technology and Engineering Research. 1 (October 1, 2026). https://doi.org/10.53525/jster.2013127.
JAMA
1.Gençay NO, Demirel YS, Tanrıver K, Ay M. Tool Path Optimization A Systematic Literature Review On Metaheuristic Algorithms. Journal of Science, Technology and Engineering Research. 2026. doi:10.53525/jster.2013127.
MLA
Gençay, Necat Ozan, et al. “Tool Path Optimization A Systematic Literature Review On Metaheuristic Algorithms”. Journal of Science, Technology and Engineering Research, no. 1, Oct. 2026, doi:10.53525/jster.2013127.
Vancouver
1.Necat Ozan Gençay, Yusuf Soner Demirel, Kürşat Tanrıver, Mustafa Ay. Tool Path Optimization A Systematic Literature Review On Metaheuristic Algorithms. Journal of Science, Technology and Engineering Research. 2026 Oct. 1;(1). doi:10.53525/jster.2013127