A hybrid ant colony optimization approach for finite element mesh decomposition
This paper examines the application of the ant colony optimization algorithm to the partitioning of unstructured adaptive meshes for parallel explicit time-stepping finite element analysis. The concept of the ant colony optimization technique for finding approximate solutions to combinatorial optimization problems is described. The application of ant colony optimization for partitioning finite element meshes based on triangular elements is described. A recursive greedy algorithm optimization method is also presented as a local optimization technique to improve the quality of the solutions given by the ant colony optimization algorithm. The partitioning is based on the recursive bisection approach. The mesh decomposition is carried out using normal and predictive modes for which the predictive mode uses a trained multilayered feed-forward neural network which estimates the number of triangular elements that will be generated after finite elements mesh generation is carried out. The performance of the proposed hybrid approach for the recursive bisection of finite element meshes is examined by decomposing two mesh examples.
ant colony optimization finite elements mesh decomposition greedy algorithm neural networks subdomain generation