Low-cost heuristics for matrix bandwidth reduction combined with a Hill-Climbing strategy

• Autor(a) principal: Oliveira, Sanderson L. Gonzaga de
• Data de Publicação: 2021
• Outros Autores: Silva, Libério M.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/49132
• topic: Bandwidth reduction; Sparse matrix; Ant colony optimization; Hyper-heuristic; Reordering algorithms; Hill-Climbing procedure; Redução da largura de banda; Matriz esparsa; Otimização de colônia de formigas; Hiper-heurística; Algoritmos de reordenação

This paper studies heuristics for the bandwidth reduction of large-scale matrices in serial computations. Bandwidth optimization is a demanding subject for a large number of scientific and engineering applications. A heuristic for bandwidth reduction labels the rows and columns of a given sparse matrix. The algorithm arranges entries with a nonzero coefficient as close to the main diagonal as possible. This paper modifies an ant colony hyper-heuristic approach to generate expert-level heuristics for bandwidth reduction combined with a Hill-Climbing strategy when applied to matrices arising from specific application areas. Specifically, this paper uses low-cost state-of-the-art heuristics for bandwidth reduction in tandem with a Hill-Climbing procedure. The results yielded on a wide-ranging set of standard benchmark matrices showed that the proposed strategy outperformed low-cost state-of-the-art heuristics for bandwidth reduction when applied to matrices with symmetric sparsity patterns.