Advances in bio-inspired computing for combinatorial optimization problems

Advances in Bio-inspired Combinatorial Optimization Problems' illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problem...

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Detalles Bibliográficos
Autor principal: Pintea, Camelia-Mihaela
Lenguaje:eng
Publicado: Springer 2013
Materias:
Engineering
Acceso en línea:https://dx.doi.org/10.1007/978-3-642-40179-4
http://cds.cern.ch/record/1620179
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author Pintea, Camelia-Mihaela
author_facet Pintea, Camelia-Mihaela
author_sort Pintea, Camelia-Mihaela
collection CERN
description Advances in Bio-inspired Combinatorial Optimization Problems' illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed.Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive a
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2013
publisher Springer
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spelling cern-16201792021-04-21T21:53:46Zdoi:10.1007/978-3-642-40179-4http://cds.cern.ch/record/1620179engPintea, Camelia-MihaelaAdvances in bio-inspired computing for combinatorial optimization problemsEngineeringAdvances in Bio-inspired Combinatorial Optimization Problems' illustrates several recent bio-inspired efficient algorithms for solving NP-hard problems.Theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots are described. Large-scale optimization problems, for example: the Generalized Traveling Salesman Problem and the Railway Traveling Salesman Problem, are solved and their results are discussed.Some of the main concepts and models described in this book are: inner rule to guide ant search - a recent model in ant optimization, heterogeneous sensitive aSpringeroai:cds.cern.ch:16201792013
spellingShingle Engineering
Pintea, Camelia-Mihaela
Advances in bio-inspired computing for combinatorial optimization problems
title Advances in bio-inspired computing for combinatorial optimization problems
title_full Advances in bio-inspired computing for combinatorial optimization problems
title_fullStr Advances in bio-inspired computing for combinatorial optimization problems
title_full_unstemmed Advances in bio-inspired computing for combinatorial optimization problems
title_short Advances in bio-inspired computing for combinatorial optimization problems
title_sort advances in bio-inspired computing for combinatorial optimization problems
topic Engineering
url https://dx.doi.org/10.1007/978-3-642-40179-4
http://cds.cern.ch/record/1620179
work_keys_str_mv AT pinteacameliamihaela advancesinbioinspiredcomputingforcombinatorialoptimizationproblems

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