A multi-population hybrid biased random key genetic algorithm for hop-constrained trees in nonlinear cost flow networks

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

Dalila B M M Fontes (Autor) FEP; Jose Fernando Goncalves (Autor) FEP

Revista

Título: Optimization Letters

Vol. 7 Nº 6

Páginas: 1303-1324

ISSN: 1862-4472

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 18 Citações

Scopus - 24 Citações

Classificação Científica

CORDIS: Ciências Sociais > Economia > Estudos de gestão > Gestão industrial

FOS: Ciências sociais > Economia e gestão

Outras Informações

ID Authenticus: P-006-8T0

DOI: 10.1007/s11590-012-0505-5

Abstract (EN): Genetic algorithms and other evolutionary algorithms have been successfully applied to solve constrained minimum spanning tree problems in a variety of communication network design problems. In this paper, we enlarge the application of these types of algorithms by presenting a multi-population hybrid genetic algorithm to another communication design problem. This new problem is modeled through a hop-constrained minimum spanning tree also exhibiting the characteristic of flows. All nodes, except for the root node, have a nonnegative flow requirement. In addition to the fixed charge costs, nonlinear flow dependent costs are also considered. This problem is an extension of the well know NP-hard hop-constrained Minimum Spanning Tree problem and we have termed it hop-constrained minimum cost flow spanning tree problem. The efficiency and effectiveness of the proposed method can be seen from the computational results reported.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 22