Particle swarm optimization–based characterization technique of nonproportional viscous damping parameter of a cantilever beam

Das, Subhajit; Mondal, Subhajit; Guchhait, Shyamal

doi:10.1177/10775463211010526

Cited by 6 publications

(4 citation statements)

References 33 publications

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“…It is based on a regular simplex and works with shirking and expanding strategy in the search space [36]. Particle swarm (PS) optimization [37,38] is also considered a well-known global algorithm for parameter estimation in this study among the other stochastic methods such as simulated annealing (SA) and genetic algorithms (GA). Stochastic (also known as probabilistic) methods can only guarantee global optimality asymptotically in the best case.…”

Section: Assessed Optimisation Methodsmentioning

confidence: 99%

Benchmarking Optimisation Methods for Model Selection and Parameter Estimation of Nonlinear Systems

Safari

Monsalve

2021

Vibration

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Characterisation and quantification of nonlinearities in the engineering structures include selecting and fitting a good mathematical model to a set of experimental vibration data with significant nonlinear features. These tasks involve solving an optimisation problem where it is difficult to choose a priori the best optimisation technique. This paper presents a systematic comparison of ten optimisation methods used to select the best nonlinear model and estimate its parameters through nonlinear system identification. The model selection framework fits the structure’s equation of motions using time-domain dynamic response data and takes into account couplings due to the presence of the nonlinearities. Three benchmark problems are used to evaluate the performance of two families of optimisation methods: (i) deterministic local searches and (ii) global optimisation metaheuristics. Furthermore, hybrid local–global optimisation methods are examined. All benchmark problems include a free play nonlinearity commonly found in mechanical structures. Multiple performance criteria are considered based on computational efficiency and robustness, that is, finding the best nonlinear model. Results show that hybrid methods, that is, the multi-start strategy with local gradient-based Levenberg–Marquardt method and the particle swarm with Levenberg–Marquardt method, lead to a successful selection of nonlinear models and an accurate estimation of their parameters within acceptable computational times.

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Section: Assessed Optimisation Methodsmentioning

confidence: 99%

Benchmarking Optimisation Methods for Model Selection and Parameter Estimation of Nonlinear Systems

Safari

Monsalve

2021

Vibration

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“…The proposed technique allows to shorten the computational time of the optimization algorithm by reducing the number of suspected structural failures. The use of the particle swarm optimization (PSO) algorithm to identify disproportionate damping parameters is presented in the work [44]. The objective function was to minimize the incomplete eigenvector which is influenced by the object external damping devices.…”

Section: Parameter Identification Of Vibrating Structuresmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Garus,

Błachowski,

Sochacki

et al. 2022

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Although the study of oscillatory motion has a long history, going back four centuries, it is still an active subject of scientific research. In this review paper prospective research directions in the field of mechanical vibrations were pointed out. Four groups of important issues in which advanced research is conducted were discussed. The first are energy harvester devices, thanks to which we can obtain or save significant amounts of energy, and thus reduce the amount of greenhouse gases. The next discussed issue helps in the design of structures using vibrations and describes the algorithms that allow to identify and search for optimal parameters for the devices being developed. The next section describes vibration in multi-body systems and modal analysis, which are key to understanding the phenomena in vibrating machines. The last part describes the properties of granulated materials from which modern, intelligent vacuum-packed particles are made. They are used, for example, as intelligent vibration damping devices.

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“…The studies mentioned above indicate that the effective seismic management of a structure relies on choosing the suitable parameters for the isolation system. Numerous optimization techniques exist to fulfill this goal (Das et al, 2022; Lin et al, 2018; Moloodpoor et al, 2021; Mortazavi and Moloodpoor, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Optimizing base isolation system parameters using a fuzzy reinforced butterfly optimization: A case study of the 2023 Kahramanmaras earthquake sequence

Kandemir,

Mortazavi

2023

Journal of Vibration and Control

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This study aims to obtain the viable parameters of base isolation systems that are utilized in buildings with seismic isolation and specially located in Turkey. To provide a more accurate representation of real-world scenarios, the study accounts for severe ground motion records simultaneously during the dynamic response analysis of the structure. Three records are taken from the 2023 Turkey earthquake (records for Elbistan, Nurdagi, and Pazarcik). The optimization model's objective function is to minimize the ratio of the roof acceleration to the peak ground acceleration. To ensure realistic outcomes, certain isolation parameters are constrained, such as maintaining the damping ratio and period within a predetermined range as well as the maximum lateral drift of the structure. An upgraded variant of the Butterfly Optimization Algorithm, known as the Fuzzy Butterfly Optimization Algorithm (FBOA), is employed to address the presented optimization model. The FBOA technique incorporates a fuzzy decision-making mechanism that adjusts its search strategy according to the prevailing conditions of the present problem. In addition, the FBOA method utilizes a new auxiliary vector called virtual butterfly, which provides information on the entire population based on each agent's quality. Consequently, FBOA transforms into a self-regulating search algorithm, eliminating the requirement for any external parameters. Results have verified the effectiveness of FBOA in detecting optimal parameters of the proposed isolation system.

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Particle swarm optimization–based characterization technique of nonproportional viscous damping parameter of a cantilever beam

Cited by 6 publications

References 33 publications

Benchmarking Optimisation Methods for Model Selection and Parameter Estimation of Nonlinear Systems

Benchmarking Optimisation Methods for Model Selection and Parameter Estimation of Nonlinear Systems

Bulletin of the Polish Academy of Sciences: Technical Sciences

Optimizing base isolation system parameters using a fuzzy reinforced butterfly optimization: A case study of the 2023 Kahramanmaras earthquake sequence

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