Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
An introduction to genetic algorithms
An introduction to genetic algorithms
Finite Elements in Analysis and Design
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Algebraic and combinatorial graph theory for optimal structural analysis
Civil and structural engineering computing: 2001
Optimal domain decomposition via p-median methodology using ACO and hybrid ACGA
Finite Elements in Analysis and Design
Advances in Engineering Software
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A new method is developed for finite element (FE) domain decomposition. This method employs a hybrid graph-genetic algorithm for graph partitioning and correspondingly bisects finite element (FE) meshes.A weighted incidence graph is first constructed for the FE mesh, denoted by G0. A coarsening process is then performed using heavy-edge matching. A sequence of such operations is employed in "n" steps, which leads to the formation of Gn with a size suitable for genetic algorithm applications.Hereafter, Gn is bisected using conventional genetic algorithm. The shortest route tree algorithm is used for the formation of the initial population in genetic algorithm. Then an uncoarsening process is performed and the results are transferred to the graph Gn-1. The initial population for genetic algorithm on Gn-1 is constructed from the results of Gn. This process is repeated until G0 is obtained in the uncoarsening operation. Employing the properties of G1, the graph G0 is bisected by the genetic algorithm.