Finite element model updating using crow search algorithm with Levy flight

Wu, Hao; Wu, Pingbo; Xu, Kai; Li, Fansong

doi:10.1002/nme.6338

Cited by 20 publications

(11 citation statements)

References 25 publications

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“…Figures 13 and 21 show that GWO converges faster than IFCSA in the middle of the iteration, but GWO will fall into the local optimum, and IFCSA can jump out of the local optimum to get a better solution. It can be seen from Figures 11,15,17, and 23 that the convergence speed and optimization accuracy of IFCSA are better than those of other algorithms. As can be seen from Figures 12,14,16,18,20,22, and 24, the performance of IFCSA algorithm is very stable.…”

Section: Comparative Experiments Of Different Intelligent Algorithms 431 High-dimensional Unimodal Function Test Resultsmentioning

confidence: 93%

“…One is to analyze and improve the parameters of the standard crow search algorithm, and the other is to integrate it with other intelligent algorithms to make up for the shortcomings of crow search algorithm. For the first kind of improvement, Wu et al proposed a crow search algorithm incorporated in Levy flight (LFCSA) [17] and applied it to update the finite element model. e chaotic sequence was used to initialize the population, so that the particles were evenly distributed in the solution space and the diversity of the population was increased.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Crow Search Algorithm Based on Improved Flower Pollination

Cheng

Huang

Chen

2021

Mathematical Problems in Engineering

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Crow search algorithm (CSA) is a new type of swarm intelligence optimization algorithm proposed by simulating the crows’ intelligent behavior of hiding and retrieving food. The algorithm has the characteristics of simple structure, few control parameters, and easy implementation. Like most optimization algorithms, the crow search algorithm also has the disadvantage of slow convergence and easy fall into local optimum. Therefore, a crow search algorithm based on improved flower pollination algorithm (IFCSA) is proposed to solve these problems. First, the search ability of the algorithm is balanced by the reasonable change of awareness probability, and then the convergence speed of the algorithm is improved. Second, when the leader finds himself followed, the cross-pollination strategy with Cauchy mutation is introduced to avoid the blindness of individual location update, thus improving the accuracy of the algorithm. Experiments on twenty benchmark problems and speed reducer design were conducted to compare the performance of IFCSA with that of other algorithms. The results show that IFCSA has better performance in function optimization and speed reducer design problem.

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Section: Comparative Experiments Of Different Intelligent Algorithms 431 High-dimensional Unimodal Function Test Resultsmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

A Novel Crow Search Algorithm Based on Improved Flower Pollination

Cheng

Huang

Chen

2021

Mathematical Problems in Engineering

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“…3) Lévy flight & Opposition-based CSA Wu et al [102] introduced a novel approach of CSA named CCSA, in which an Lévy flight was used. The novel algo-rithm was tested on two different models: a simple structure (beam) and a complex structure.…”

Section: ) Modified Csamentioning

confidence: 99%

Crow Search Algorithm: Theory, Recent Advances, and Applications

et al. 2020

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In this paper, a comprehensive overview of the Crow Search Algorithm (CSA) is introduced with detailed discussions, which is intended to keep researchers interested in swarm intelligence algorithms and optimization problems. CSA is a new swarm intelligence algorithm recently developed, which simulates crow behavior in storing excess food and retrieving it when needed. In the optimization theory, the crow is the searcher, the surrounding environment is the search space, and randomly storing the location of food is a feasible solution. Among all food locations, the location where the most food is stored is considered to be the global optimal solution, and the objective function is the amount of food. By simulating the intelligent behavior of crows, CSA tries to find optimal solutions to various optimization problems. It has gained a considerable interest worldwide since its advantages like simple implementation, a few numbers of parameters, flexibility, etc. This survey introduces a comprehensive variant of CSA, including hybrid, modified, and multi-objective versions. Furthermore, based on the analyzed papers published in the literature by some publishers such as IEEE, Elsevier, and Springer, the comprehensive application scenarios of CSA such as power, computer science, machine learning, civil engineering have also been reviewed. Finally, the advantages and disadvantages of CSA have been discussed by conducting some comparative experiments with other similar published peers.

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“…Previous studies [17][18][19] have shown that the wheel polygonization will lead to a surge in vibration response of various components of the vehicle. Wu et al [20,21] used an intelligent algorithm to improve the accuracy of the gearbox model and studied the influence of harmonic traction torque on the vibration and stress of the gearbox. Huang et al [22] established a dynamic model of a high-speed train and a finite element model of the gearbox housing and analyzed the influence of internal excitation (the periodic variation of meshing stiffness) and external excitation (rail excitation, harmonic traction torque).…”

Section: Introductionmentioning

confidence: 99%

Vibration and Stress Response of High-Speed Train Gearboxes under Different Excitations

Zhu

Sun

2022

Applied Sciences

Self Cite

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The vibration data of the gearbox on a high-speed train was measured, and the vibration characteristics were analyzed in this paper. The dynamic stress of the gearbox under the internal and external excitation was examined by a railway vehicle dynamic model with a flexible gearbox and a flexible wheelset. The ideal 20th polygonal wear was considered, and dynamic stresses of the gearbox under different polygonal wear amplitudes were calculated. The gear transmission model was established to study the dynamic stress of the gearbox under the influence of the time-varying stiffness of the gear meshing. Based on the rigid–flexible coupling model, and considering the influence of wheel polygonization, gear meshing time-varying stiffness, and wheelset elastic deformation, the dynamic stress of the gearbox was investigated with consideration of the measured polygonal wear and measured rail excitation. The results show that the dynamic stress of the gearbox is dominated by the wheel polygonization. Moreover, not only the wheel polygonization excites the resonance of the gearbox, but also the flexible deformation of the wheelset leads to the deformation of the gearbox, which also increases the dynamic stress of the gearbox. Within the resonant bandwidth of the frequency, the amplitude of the dynamic stresses in the gearbox will increase considerably compared with the normal case.

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Finite element model updating using crow search algorithm with Levy flight

Cited by 20 publications

References 25 publications

A Novel Crow Search Algorithm Based on Improved Flower Pollination

A Novel Crow Search Algorithm Based on Improved Flower Pollination

Crow Search Algorithm: Theory, Recent Advances, and Applications

Vibration and Stress Response of High-Speed Train Gearboxes under Different Excitations

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