Optimization of Excitation Frequencies of a Gearbox Using Algorithms Inspired by Nature

Gutiérrez, Stephanie Virginia Camacho; Jáuregui-Correa, Juan Carlos; Dominguez, A.

doi:10.1007/s42417-019-00149-6

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…For example, the genetic algorithms, the particle swarm optimization, the ant colony optimization, and the differential evolution, among others. In this paper, the particle swarm algorithm was chosen because it is easy to implement and has fast convergence [30].…”

Section: The Particle Swarm Optimizationmentioning

confidence: 99%

“…The elements of X i are the new control points. If the perimeter is higher or less than the original, then a penalization is applied by adding or subtracting an amount from the obtained perimeter value, Equations (29) and (30), where k represents a proportional constant to the difference between the value of I and I or . To update the best local position, L i , the penalized I is compared with the best saved local perimeter; if the new I value is better than the saved, the best local position L i is updated.…”

Section: The Particle Swarm Optimizationmentioning

confidence: 99%

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An Application of Isogeometric Analysis and Boundary Integral Element Method for Solving Nonlinear Contact Problems

et al. 2020

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A novel method to solve nonlinear contact between two bodies with plane strain behavior is presented in this paper. This method is based on the Boundary Integral Equation (BIE) and the Isogeometric Analysis (IGA). Unlike works that divide the boundary into elements, this method evaluates it as a single element, reducing the degrees of freedom in the solution. Moreover, the Particle Swarm Optimization Algorithm (PSO) was used in order to estimate the deformation when two bodies are in contact and there is penetration between them. The obtained results were compared with the Finite Element Method (FEM) and the Hertz contact equation.

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Section: The Particle Swarm Optimizationmentioning

confidence: 99%

Section: The Particle Swarm Optimizationmentioning

confidence: 99%