Efficiency of Jaya algorithm for solving the optimization-based structural damage identification problem based on a hybrid objective function

Dinh-Cong, Du; Ho-Huu, V.; Vo-Duy, T.; Quyen, Ngo-Thi Hong; Nguyen-Thoi, T.

doi:10.1080/0305215x.2017.1367392

Cited by 102 publications

(34 citation statements)

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“…BBBC [144,145,146,147,148,149] was more often utilized than ICA [150,151], ACO [152,153], JAYA [154,155] and machine learning [156]. However, there are quite less studies on inverse problems employing BBBC than for GA, DE, SA, HS and PSO.…”

Section: Introduction and Theoretical Backgroundmentioning

confidence: 99%

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

2019

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This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms—denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)—is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines.

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Section: Introduction and Theoretical Backgroundmentioning

confidence: 99%

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

2019

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“…In this work, we use a hybrid objective function, proposed by Dinh-Cong et al [12], is a combination of two dierent objective functions for nding the damage sites and severities [11]. This objective function is given as…”

Section: Hybrid Objective Functionmentioning

confidence: 99%

“…Nobahari [10] introduced an optimization method called echolocation search algorithm (ESA) for damage localization and estimation in trusses using changes in modal frequencies. Dinh-Cong et al [11] presented a damage identication approach utilizing Jaya algorithm and hybrid objective function to nd the damage sites and sizes of simple structures like truss and frames. Dinh-Cong et al [12] proposed an ecient multi-stage optimization approach for damage assessment in plate-like structures using a modied dierential evolution algorithm (MS-MDE).…”

Section: Introductionmentioning

confidence: 99%

Damage Detection of 2D Frame Structures using Incomplete Measurements by Optimization Procedure and Model Reduction

Dinh-Cong

Pham-Duy²,

Nguyen-Thoi³

2018

J. ADV. ENG. COMPUT.

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The article presents an effective method for damage assessment of 2D frame structures using incomplete modal data by optimization procedure and model reduction technique. In this proposed method, the structural damage detection problem is defined as an optimization problem, in which a hybrid objective function and the damage severity of all elements are considered as the objective function and the continuous design variables, respectively. The teaching-learning-based optimization (TLBO) algorithm is applied as a powerful optimization tool to solve the problem. In addition, owing to the use of incomplete measurements, an improved reduction system (IRS) technique is adopted to reduce the mass and stiffness matrices of structural finite element model. The efficiency and robustness of the proposed method are validated with a 4-storey (3 bay) steel plane frame involving several damage scenarios without and with measurement noise. The obtained results clearly demonstrate that even the incompleteness and noisy environment of measured modal data, the present method can work properly in locating and estimating damage of the frame structure by utilizing only the first five incomplete modes' data. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…Computational results revealed that Jaya has considerable potential in the context of machining performance optimization problems. Few applications of Jaya can also be found in design related problems (Du et al 2017;Rao & More, 2017). Moreover, Jaya has also received successful applications in the optimization of power transmission and electrical related problems.…”

Section: Introductionmentioning

confidence: 99%

A discrete Jaya algorithm for permutation flow-shop scheduling problem

Mishra

Shrivastava

2020

10.5267/j.ijiec

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Jaya algorithm has recently been proposed, which is simple and efficient meta-heuristic optimization technique and has received a great attention in the world of optimization. It has been successfully applied to some thermal, design and manufacturing associated optimization problems. This paper aims to analyze the performance of Jaya algorithm for permutation flowshop scheduling problem which is a well-known NP-hard optimization problem. The objective is to minimize the makespan. First, to make Jaya algorithm adaptive to the problem, a random priority is allocated to each job in a permutation sequence. Second, a job priority vector is converted into job permutation vector by means of Largest Order Value (LOV) rule. An exhaustive comparative study along with statistical analysis is performed by comparing the results with public benchmarks and other competitive heuristics. The key feature of Jaya algorithm of simultaneous movement towards the best solution and going away from the worst solution enables it to avoid being trapped in the local optima. Furthermore, the uniqueness of Jaya algorithm compared with any other evolutionary based optimization technique is that it is totally independent of specific parameters. This substantially reduces the computation effort and numerical complexity. Computational results reveal that Jaya algorithm is efficient in most cases and has considerable potential for permutation flow-shop scheduling problems.

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Efficiency of Jaya algorithm for solving the optimization-based structural damage identification problem based on a hybrid objective function

Cited by 102 publications

References 50 publications

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization

Damage Detection of 2D Frame Structures using Incomplete Measurements by Optimization Procedure and Model Reduction

A discrete Jaya algorithm for permutation flow-shop scheduling problem

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