Carpenter Optimization Algorithm: A Human-inspired Metaheuristic for Robust and Efficient Constrained Optimization

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dc.contributor.authorDinler, Özlem Batur
dc.contributor.authorBektemyssova, Gulnara
dc.contributor.authorŞahin, Canan Batur
dc.contributor.authorMontazeri, Zeinab
dc.contributor.authorDehghani, Mohammad
dc.contributor.authorSmerat, Aseel
dc.contributor.authorEguchi, Kei
dc.date.accessioned2026-06-19T06:32:04Z
dc.date.available2026-06-19T06:32:04Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThis paper introduces the Carpenter Optimization Algorithm (COA), a novel human-inspired metaheuristic designed to efficiently solve complex, high-dimensional, and constrained optimization problems. COA draws direct inspiration from the systematic behaviors of skilled carpenters, who initially perform broad cuts to explore raw materials and subsequently execute precise refinements to achieve high-quality outcomes. These behaviors are mathematically mapped into exploration and exploitation phases, where stochastic global modifications mimic broad exploratory actions, and targeted incremental adjustments refine promising solutions. Unlike traditional metaheuristics, COA achieves a robust balance between exploration and exploitation while requiring minimal control parameters, enhancing both adaptability and computational efficiency. The algorithm was rigorously evaluated on 22 constrained benchmark functions from the CEC 2011 suite and compared against nine well-established metaheuristics. The results demonstrate that COA consistently outperforms all competitors in terms of solution quality, convergence speed, stability, and robustness, achieving the best mean, median, and best values across all test problems. Statistical analyses, including standard deviation, rank-based evaluation, and pairwise Wilcoxon tests, confirm the significance and reproducibility of these results, while boxplot visualizations highlight controlled variability and narrow interquartile ranges, even for large-scale and multimodal problems. The findings suggest that COA’s behaviorally grounded design provides a practical and explainable framework for real-world optimization tasks. Future research directions include extending COA to multi-objective, dynamic, and large-scale industrial problems, integrating hybrid strategies or adaptive mechanisms, and conducting theoretical analyses of convergence and parameter sensitivity. Overall, COA represents a promising addition to the metaheuristic optimization landscape, offering both conceptual clarity and high practical performance. This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. License details: https://creativecommons.org/licenses/by-sa/4.0/
dc.identifier.doi10.22266/ijies2025.1231.21
dc.identifier.endpage357
dc.identifier.issn2185-310X
dc.identifier.issue11
dc.identifier.scopus2-s2.0-105022422912
dc.identifier.scopusqualityQ2
dc.identifier.startpage344
dc.identifier.urihttps://doi.org/10.22266/ijies2025.1231.21
dc.identifier.urihttps://hdl.handle.net/20.500.12899/4930
dc.identifier.volume18
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIntelligent Network and Systems Society
dc.relation.ispartofInternational Journal of Intelligent Engineering and Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_Scopus_20260612
dc.subjectAlgorithm Stability
dc.subjectCarpenter Optimization Algorithm
dc.subjectConstrained Optimization
dc.subjectExploration And Exploitation
dc.subjectHigh-Dimensional Problems
dc.subjectHuman-Inspired Algorithm
dc.subjectMetaheuristic
dc.titleCarpenter Optimization Algorithm: A Human-inspired Metaheuristic for Robust and Efficient Constrained Optimization
dc.typeArticle

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