OPTIMAL DECOMPOSITION OF FINITE ELEMENT MESHES VIA K-MEDIAN METHODOLOGY AND DIFFERENT METAHEURISTICS

In this paper the performance of four well-known metaheuristics consisting of Artificial Bee Colony (ABC), Biogeographic Based Optimization (BBO), Harmony Search (HS) and Teaching Learning Based Optimization (TLBO) are investigated on optimal domain decomposition for parallel computing. A clique graph is used for transforming the connectivity of a finite element model (FEM) into that of the corresponding graph, and k-median approach is employed. The performance of these methods is investigated through four FE models with different topology and number of meshes. A comparison of the numerical results using different algorithms indicates, in most cases the BBO is capable of performing better or identical using less time with equal computational effort.

OPTIMAL DECOMPOSITION OF FINITE ELEMENT MESHES VIA K-MEDIAN METHODOLOGY AND DIFFERENT METAHEURISTICS Keywords:

domain decomposition , Finite elements meshes , graph theory , optimization , metaheuristic algorithms , k-median , k-means++.