An Improved particle swarm optimization algorithm based on chaotic BAS strategy

Zhang, Liyue; Wang, Yi; Wang, Niantai; Lu, Ze-Dan; Liu, Haoran; Zhang, Yun; Zhang, Lei; Gu, Liheng; Guo, Ningjun; Li, Yan

doi:10.1109/icisce50968.2020.00066

Cited by 1 publication

(1 citation statement)

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“…A larger inertia weight w is advantageous for global search, while a smaller inertia weight is advantageous for local search. By adjusting the inertia weight in a timely manner based on the optimization degree λ of each optimization search, it is possible to search for the best particle [21].…”

Section: Identification Of the Parameters Of The Viscoelastic Elementsmentioning

confidence: 99%

Parameter Identification and Dynamic Characteristic Research of a Fractional Viscoelastic Model for Sub-Frame Bushing

Chen,

Zhou

et al. 2023

Vehicles

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This research focused on the rubber bushings of the rear sub-frame in an electric vehicle. A dynamic model was developed to represent the bushing, incorporating an elastic element, a frictional element, and a viscoelastic element arranged in series using a fractional-order Maxwell and a Kelvin–Voigt model. To identify the parameters of the bushing model, an improved adaptive chaotic particle swarm optimization algorithm was employed, in conjunction with dynamic stiffness test data obtained at an amplitude of 0.2 mm. The test data obtained at different amplitudes (0.2 mm, 0.3 mm, 0.5 mm, and 1 mm) were fitted to the model, resulting in fitting errors of 1.13%, 4.07%, 4.42%, and 28.82%, respectively, when compared to the corresponding test data in order to enhance the accuracy of the model fitting; the Sobol sensitivity analysis method was utilized to analyze the parameter sensitivity of the model. Following the analysis, the parameters α, β, and k2, which exhibited high sensitivity, were re-identified. This re-identification process led to a reduction in the fitting error at the 1 mm amplitude to 7.45%. The improved accuracy of the model plays a crucial role in enhancing the simulation accuracy of design of experiments (DOE) analysis and verifying the vehicle’s performance under various conditions, taking into account the influence of the bushing.

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Section: Identification Of the Parameters Of The Viscoelastic Elementsmentioning

confidence: 99%