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In this paper a Guided Tabu Search (GTS) is utilized for optimal nodal ordering of finite element models (FEMs) leading to small profile for the stiffness matrices of the models. The search strategy is accelerated and a graph-theoretical approach is used as guidance. The method is evaluated by minimization of graph matrices pattern equivalent to stiffness matrices of finite element models. Comparison of the results with those of some powerful methods, confirms the robustness of the algorithm.

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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.