Quality improvement of 3-D FE mesh by a BCA approach

Purpose - The aim of this work is to show how evolutionary computation can improve the quality of 3D-FE mesh that is a crucial task for field evaluations using 3-D FEM analysis. Design/methodology/approach - The evolutionary approach used for optimizing 3D mesh generation is based on the bacterial chemotaxis algorithm (BCA). The objective function corresponds to the virtual bacterium best habitat, and the motion rules followed by each virtual bacterium are inspired to the natural behaviour of bacteria in real habitat. Findings - The obtained results show that the present approach returns good accuracy performances with low-computational costs. Practical implications - The procedure is robust and converges for all the practical cases examined for validation. Originality/value - The adoption of a correct optimization algorithm is fundamental to obtain good performances in terms of robustness of the results and the low-computational costs. In this sense, the BCA is a valid instrument for improving the quality of 3D-FE mesh.