Name
Abstract | ||
|---|---|---|
The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous reordering of the rows and the columns of a square matrix such that the nonzero entries are collected within a band of small width close to the main diagonal. The MBMP is a NP-complete problem, with applications in many scientific domains, linear systems, artificial intelligence, and real-life situations in industry, logistics, information recovery. The complex problems are hard to solve, that is why any attempt to improve their solutions is beneficent. Genetic algorithms and ant-based systems are Soft Computing methods used in this paper in order to solve some MBMP instances. Our approach is based on a learning agent-based model involving a local search procedure. The algorithm is compared with the classical Cuthill-McKee algorithm, and with a hybrid genetic algorithm, using several instances from Matrix Market collection. Computational experiments confirm a good performance of the proposed algorithms for the considered set of MBMP instances. On Soft Computing basis, we also propose a new theoretical Reinforcement Learning model for solving the MBMP. |
Year | Venue | Keywords |
|---|---|---|
2013 | Informatica | matrix bandwidth minimization problem,natorial optimization,reinforcement learning,soft computing |
DocType | Volume | Issue |
Journal | 24 | 2 |
ISSN | Citations | PageRank |
0868-4952 | 3 | 0.38 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriela Czibula | 1 | 80 | 19.53 |
| Gloria Cerasela Crisan | 2 | 83 | 9.08 |
| Camelia-Mihaela Pintea | 3 | 102 | 16.15 |
| István Gergely Czibula | 4 | 91 | 11.79 |