Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00521-006-0041-2
Title: Genetic algorithms in mesh optimization for visualization and finite element models
Authors: Chong, C.S.
Lee, H.P. 
Kumar, A.S. 
Keywords: Finite element modeling
Genetic algorithms
Mesh optimization
Issue Date: Jun-2006
Source: Chong, C.S., Lee, H.P., Kumar, A.S. (2006-06). Genetic algorithms in mesh optimization for visualization and finite element models. Neural Computing and Applications 15 (3-4) : 366-372. ScholarBank@NUS Repository. https://doi.org/10.1007/s00521-006-0041-2
Abstract: This paper investigates the use of a genetic algorithm (GA) to perform the large-scale triangular mesh optimization process. This optimization process consists of a combination of mesh reduction and mesh smoothing that will not only improve the speed for the computation of a 3D graphical or finite element model, but also improve the quality of its mesh. The GA is developed and implemented to replace the original mesh with a re-triangulation process. The GA features optimized initial population, constrained crossover operator, constrained mutation operator and multi-objective fitness evaluation function. While retaining features is important to both visualization models and finite element models, this algorithm also optimizes the shape of the triangular elements, improves the smoothness of the mesh and performs mesh reduction based on the needs of the user.
Source Title: Neural Computing and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/60402
ISSN: 09410643
DOI: 10.1007/s00521-006-0041-2
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