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A Parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics
Ionescu, Dragos; Juan Pérez, Angel Alejandro; Faulín, Javier; Ferrer Biosca, Alberto
Universitat Politècnica de Catalunya. Departament de Matemàtica Aplicada I; Universitat Politècnica de Catalunya. GNOM - Grup d´Optimització Numèrica i Modelització
This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex conguration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases
Peer Reviewed
Àrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica discreta::Combinatòria
Heuristic algorithms
Combinatorial optimization
Heurística
Optimització combinatòria
Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming
Classificació AMS::65 Numerical analysis::65K Mathematical programming, optimization and variational techniques
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Article - Draft
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