YUKI Algorithm and POD-RBF for Elastostatic and dynamic crack identification. Benaissa, B., Hocine, N., Khatir, S., Riahi, M., & Mirjalili, S. Journal of Computational Science, 2021. ![YUKI Algorithm and POD-RBF for Elastostatic and dynamic crack identification [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper proposes a new metaheuristic algorithm with a search space reduction capability guided by simple formalism. The search population focuses partially on the inside the local search area while the rest explore globally, looking for better search areas. We call the new algorithm by YUKI Algoritm (YA) and employ it in a crack identification problem. With the aid of a set of measurements taken on the defected structure, we aim at identifying the crack parameters such as length and orientation. To this end, we use the so-called model reduction technique through Proper orthogonal Decomposition (POD) endorsed with Radial Basic Function (RBF), which helps in predicting (numerically) the measurement at new points (out of the set of sensors) via interpolation. This method is widely used in this context and was proven very effective computational-wise. In our study of the performance of YA, we deal with two cases; Firstly, in the case of the Elastostatic study. And secondly, in the case of dynamic analysis. We compare the performance of the suggested algorithm with the performance of well-known optimization methods, such as Teaching Learning Based Optimization (TLBO), Cuckoo Search (CS), and the Gray Wolf Optimizer (GWO). The results show that YA provides accurate and faster results compared to the mentioned algorithms. © 2021 Elsevier B.V.
@ARTICLE{Benaissa2021,
author={Benaissa, B. and Hocine, N.A. and Khatir, S. and Riahi, M.K. and Mirjalili, S.},
title={YUKI Algorithm and POD-RBF for Elastostatic and dynamic crack identification},
journal={Journal of Computational Science},
year={2021},
volume={55},
doi={10.1016/j.jocs.2021.101451},
art_number={101451},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116494164&doi=10.1016%2fj.jocs.2021.101451&partnerID=40&md5=f93d2a200718442024311432e5643b8a},
affiliation={Toyota Technological Institute, Department of Mechanical Systems Engineering, Design Engineering Lab, 468-8511 Aichi, Nagoya, Tempaku Ward, Hisakata, 2 Chome-12-1, Japan; INSA CVL, Univ. Tours, Univ. Orlans, LaM, 3 rue de la Chocolaterie, Blois, Cedex, CS 23410, 41034, France; Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Technologiepark Zwijnaarde 903, Zwijnaarde, B-9052, Belgium; Faculty of Civil Engineering, Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam; Department of Mathematics, Khalifa University of Sciences and Technology, P.O. Box 127788, Abu Dhabi, United Arab Emirates; Emirates Nuclear Technology Center (ENTC), Khalifa University of Science and Technology, United Arab Emirates; Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD, 4006, Australia; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea},
abstract={This paper proposes a new metaheuristic algorithm with a search space reduction capability guided by simple formalism. The search population focuses partially on the inside the local search area while the rest explore globally, looking for better search areas. We call the new algorithm by YUKI Algoritm (YA) and employ it in a crack identification problem. With the aid of a set of measurements taken on the defected structure, we aim at identifying the crack parameters such as length and orientation. To this end, we use the so-called model reduction technique through Proper orthogonal Decomposition (POD) endorsed with Radial Basic Function (RBF), which helps in predicting (numerically) the measurement at new points (out of the set of sensors) via interpolation. This method is widely used in this context and was proven very effective computational-wise. In our study of the performance of YA, we deal with two cases; Firstly, in the case of the Elastostatic study. And secondly, in the case of dynamic analysis. We compare the performance of the suggested algorithm with the performance of well-known optimization methods, such as Teaching Learning Based Optimization (TLBO), Cuckoo Search (CS), and the Gray Wolf Optimizer (GWO). The results show that YA provides accurate and faster results compared to the mentioned algorithms. © 2021 Elsevier B.V.},
author_keywords={Crack identification; Inverse problem; POD-RBF; Static and dynamic analysis; Yuki Algorithm},
document_type={Article}
}
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We compare the performance of the suggested algorithm with the performance of well-known optimization methods, such as Teaching Learning Based Optimization (TLBO), Cuckoo Search (CS), and the Gray Wolf Optimizer (GWO). 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