Tool path optimization using metaheuristic algorithms: A systematic literature review

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dc.authorid0009-0007-3154-4245
dc.authorid0009-0000-4353-1248
dc.authorid0000-0002-1723-4108
dc.authorid0000-0002-7672-1846
dc.contributor.authorGençay, Necat Ozan
dc.contributor.authorDemirel, Yusuf Soner
dc.contributor.authorTanrıver, Kürşat
dc.contributor.authorAy, Mustafa
dc.date.accessioned2026-10-08T08:00:27Z
dc.date.available2026-10-08T08:00:27Z
dc.date.issued2026
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümü
dc.description.abstractThis 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 39 application studies met the inclusion criteria and were classified according to the algorithm family employed, then synthesized within a comparative framework alongside nine founding papers used to define the underlying algorithms. Approximately 44% 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, with the best-performing hybrids reporting non-productive time reductions of up to 86.65%. 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.
dc.identifier.citationGençay, N. O., Demirel, Y. S., Tanrıver, K., & Ay, M. (2026). Tool path optimization using metaheuristic algorithms: A systematic literature review. Journal of Science, Technology and Engineering Research, (1), pp. 1-20. https://doi.org/10.53525/jster.2013127
dc.identifier.doi10.53525/jster.2013127
dc.identifier.endpage20
dc.identifier.issn2717-8404
dc.identifier.issue1
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.53525/jster.2013127
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1662
dc.institutionauthorTanrıver, Kürşat
dc.institutionauthorid0000-0002-1723-4108
dc.language.isoen
dc.publisherMehmet Bulut
dc.relation.ispartofJournal of Science, Technology and Engineering Research
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMulti-Hole Drilling
dc.subjectTool Path Optimization
dc.subjectAnt Colony Optimization
dc.subjectBees Algorithm
dc.subjectGrey Wolf Optimizer
dc.titleTool path optimization using metaheuristic algorithms: A systematic literature review
dc.typeReview Article
dspace.entity.typePublication

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