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Advances in bio-inspired computing for combinatorial optimization problems

Autor: Camelia-Mihaela Pintea
Editora: Berlin : Springer, [2013?] ©2014
Séries: Intelligent systems reference library, v.57
Edição/Formato   e-book : Documento : InglêsVer todas as edições e formatos
Base de Dados:WorldCat
Resumo:
"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  Ler mais...
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Detalhes

Gênero/Forma: Electronic books
Tipo de Material: Documento, Recurso Internet
Tipo de Documento: Recurso Internet, Arquivo de Computador
Todos os Autores / Contribuintes: Camelia-Mihaela Pintea
ISBN: 9783642401794 3642401791
Número OCLC: 857586057
Descrição: 1 online resource (x, 188 pages) : illustrations.
Conteúdos: Part I. Biological Computing and Optimization --
Part II. Ant Algorithms --
Part III. Bio-inspired Multi-Agent Systems --
Part IV. Applications with Bio-inspired Algorithms --
Part V. Conclusions and Remarks.
Título da Série: Intelligent systems reference library, v.57
Responsabilidade: Camelia-Mihaela Pintea.
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Resumo:

This book illustrates recent bio-inspired efficient algorithms for solving NP-hard problems. Discusses theoretical bio-inspired concepts and models, in particular for agents, ants and virtual robots,  Ler mais...

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schema:description"Part I. Biological Computing and Optimization -- Part II. Ant Algorithms -- Part III. Bio-inspired Multi-Agent Systems -- Part IV. Applications with Bio-inspired Algorithms -- Part V. Conclusions and Remarks."
schema: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 ants; virtual sensitive robots; ant-based techniques for static and dynamic routing problems; stigmergic collaborative agents and learning sensitive agents. This monograph is useful for researchers, students and all people interested in the recent natural computing frameworks. The reader is presumed to have knowledge of combinatorial optimization, graph theory, algorithms and programming. The book should furthermore allow readers to acquire ideas, concepts and models to use and develop new software for solving complex real-life problems."
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