Improvement of the computational efficiency of metaheuristic algorithms for the crack detection of cantilever beams using hybrid methods

Firouzi, Behnam; Abbasi, Ahmad; Şendur, Polat

doi:10.1080/0305215x.2021.1919887

Cited by 12 publications

(3 citation statements)

References 51 publications

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“…Moreover, some studies have been performed to evaluate the performance of the PSO algorithm using statistical analysis. In Firouzi et al, 36 the performance of different optimization algorithms is compared using statistical post hoc analysis. The PSO algorithm shows relatively accurate performance in finding the optimal solutions.…”

Section: Design Optimization Of the Solenoid Actuator With A Particle...mentioning

confidence: 99%

Design optimization of a solenoid actuator using particle swarm optimization algorithm with multiple objectives

Abedinifar

Ertuğrul

Tayyar

2022

Advances in Mechanical Engineering

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Solenoid actuators are well-known components that convert electromagnetic energy into mechanical energy. For control purposes, it is requested to have a high magnetic force that stays almost constant in the working region of the actuator. To meet these requirements, it is necessary to have an optimal geometrical design of the actuator. In this study, the following steps are performed to optimize the geometry of the solenoid actuator. The Finite Element Analysis (FEA) is performed, and the results of the simulation is verified with the experimental data. The effect of all geometrical parameters on the characteristics of the magnetic force is investigated. The parameters that highly affect the magnetic force are chosen as design optimization parameters. Then, the Particle Swarm Optimization (PSO) algorithm is realized to find optimal parameters. The algorithm consists of two objective functions being combined into a single objective function. It includes a higher and more consistent magnetic force in the effective working region of the solenoid. Finally, the solenoid actuator with optimized parameters is manufactured, and the results are compared. They show that the optimized solenoid actuator satisfies one of the objective functions, and magnetic force stays almost constant in the working region of the solenoid actuator.

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Section: Design Optimization Of the Solenoid Actuator With A Particle...mentioning

confidence: 99%

Design optimization of a solenoid actuator using particle swarm optimization algorithm with multiple objectives

Abedinifar

Ertuğrul

Tayyar

2022

Advances in Mechanical Engineering

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“…Zai et al [26] investigated the changes in frequency, damping, and vibration response amplitude under thermomechanical loads and proposed a damage quantification method for cantilever beams. With the development of intelligent algorithms and signal processing technologies, bispectral analysis [27], neural network [27,28], spatial wavelet analysis [29], full wavelet scalogram [28], metaheuristic algorithm [30], Bayesian parameter estimation [31], and deep learning [32] have shown significant promise in the damage detection of cantilever beams.…”

Section: Introductionmentioning

confidence: 99%

A Novel Low-Velocity Impact Region Identification Method for Cantilever Beams Using a Support Vector Machine

Wang

Kang

Xiao

et al. 2022

Mathematical Problems in Engineering

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Damage induced by a low-velocity impact can reduce the stability and reliability of structures. In this study, a novel low-velocity impact region identification method based on the spectral peak frequency (SPF) and support vector machine (SVM) is proposed to identify the low-velocity impact regions on a steel cantilever beam. A low-velocity impact region identification system of the cantilever beam is established by applying fiber Bragg grating (FBG) sensors, and only 2 sensors are used in this system. The power spectral density functions of the impact response signal are smoothed using the linear weighting method to remove pseudospectral peak frequencies, and then, SPFs are extracted as the features. For 25 low-velocity impact regions with dimensions of 30 mm × 10 mm, the results show that the recognition rate obtained by the proposed method is 100% and the feature vector consisting of the first two SPFs with the largest amplitude has the highest recognition rate. Through the comparative study, it is found that the recognition rate of SVM is higher than that of the probabilistic neural network (PNN) and extreme learning machine (ELM) for low-velocity impact area recognition of cantilever beams. As a result, the low-velocity impact region identification method of this paper can be applied to the real-time health monitoring of cantilever beam structures.

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“…Improving the optimization algorithms via hybridization with other algorithms is a common approach for improving their accuracy and efficiency [41,42,51,[56][57][58]. Song et al [59] proposed an improved version of the HHO algorithm, in which by adding Gaussian mutation and dimension decision strategies, the exploitation and exploration phases of the HHO algorithm were improved.…”

Section: Introductionmentioning

confidence: 99%

Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings

Abbasi

Firouzi

Şendur

et al. 2021

Engineering with Computers

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Bearing is one of the most fundamental components of rotary machinery, and its fatigue life is a crucial factor in designing. The design optimization of tapered roller bearing (TRB) is a complex design problem because various arrays of designing parameters and functional requirements should be fulfilled. Since there are many design variables and nonlinear constraints, presenting an optimal design of TRBs poses some challenges for metaheuristic algorithms. The Harris hawks optimization (HHO) algorithm is a robust nature-inspired method with unique exploitation and exploration phases due to its time-varying structure. However, this metaheuristic algorithm may still converge to local optima for more challenging problems such as the design of TRBs. Therefore, this study aims to improve the accuracy and efficiency of the shortcomings of this algorithm. The performance of the proposed algorithm is first evaluated for the TRB optimization problem. The TRB optimization design has nine design variables and 26 constraints because of geometrical dimensions and strength conditions. The productivity of the proposed method is compared with diverse metaheuristic algorithms in the literature. The results demonstrate the significant development of dynamic load capacity in comparison to the standard value. Furthermore, the enhanced version of the HHO algorithm presented in this study is benchmarked with various well-known engineering problems. For supplementary materials regarding algorithms in this research, readers can refer to https://aliasgharheidari.com .

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Improvement of the computational efficiency of metaheuristic algorithms for the crack detection of cantilever beams using hybrid methods

Cited by 12 publications

References 51 publications

Design optimization of a solenoid actuator using particle swarm optimization algorithm with multiple objectives

Design optimization of a solenoid actuator using particle swarm optimization algorithm with multiple objectives

A Novel Low-Velocity Impact Region Identification Method for Cantilever Beams Using a Support Vector Machine

Multi-strategy Gaussian Harris hawks optimization for fatigue life of tapered roller bearings

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