Metaheuristic to Optimize Computational Convergence in Convection-Diffusion and Driven-Cavity Problems

Juana Enríquez-Urbano, Marco Antonio Cruz-Chávez, Rafael Rivera-López, Martín H. Cruz-Rosales, Yainier Labrada-Nueva and Marta Lilia Eraña-Díaz
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Juana Enríquez-Urbano: Research Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Cuernavaca 62209, Mexico
Marco Antonio Cruz-Chávez: Research Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Cuernavaca 62209, Mexico
Rafael Rivera-López: Computation and Systems Department, National Technological Institute of Mexico/Veracruz Technological Institute, Veracruz 91860, Mexico
Martín H. Cruz-Rosales: Faculty of Accounting, Administration & Informatics, UAEM, Cuernavaca 62209, Mexico
Yainier Labrada-Nueva: Research Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Cuernavaca 62209, Mexico
Marta Lilia Eraña-Díaz: Research Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Cuernavaca 62209, Mexico

Mathematics, 2021, vol. 9, issue 7, 1-19

Abstract: This work presents an optimization proposal to better the computational convergence time in convection-diffusion and driven-cavity problems by applying a simulated annealing (SA) metaheuristic, obtaining optimal values in relaxation factors ( RF ) that optimize the problem convergence during its numerical execution. These relaxation factors are tested in numerical models to accelerate their computational convergence in a shorter time. The experimental results show that the relaxation factors obtained by the SA algorithm improve the computational time of the problem convergence regardless of user experience in the initial low-quality RF proposal.

Keywords: overlaps; neighborhood structure; amorphous shapes; paper waste; resource allocation; perturbations (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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