Finite element mesh partitioning using neural networks
Advances in Engineering Software
Mesh Partitioning: A Multilevel Balancing and Refinement Algorithm
SIAM Journal on Scientific Computing
A parallel implementation of ant colony optimization
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Parallelization Strategies for Ant Colony Optimization
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Solving the mesh-partitioning problem with an ant-colony algorithm
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Optimal domain decomposition via p-median methodology using ACO and hybrid ACGA
Finite Elements in Analysis and Design
A survey on parallel ant colony optimization
Applied Soft Computing
Hi-index | 0.00 |
The k-way finite element mesh (FEM) decomposition problem is an NP-complete problem, which consists of finding a decomposition of a FEM into k balanced submeshes such that the number of cut edges is minimized. The multilevel ant-colony algorithm (MACA) is quite new and promising hybrid approach for solving different type of FEM-decomposition problems. The MACA is a swarm-based algorithm and therefore inherently suitable for parallel processing on many levels. Motivated by the good performance of the MACA and the possibility to improve it's performance (computational cost and/or solution quality), in this paper we discuss the results of parallelizing the MACA on largest scale (on colony level). Explicitly, we present the distributed MACA (DMACA) approach, which is based on the idea of parallel independent runs enhanced with cooperation in form of a solution exchange among the concurrent searches. Experimental evaluation of the DMACA on a larger set of benchmark FEM-decomposition problems shows that the DMACA compared to the MACA can obtain solutions of equal quality in less computational time.