Ant colony optimization /:
An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can pr...
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Cambridge, Mass. :
MIT Press,
©2004.
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Online-Zugang: | Volltext |
Zusammenfassung: | An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms. |
Beschreibung: | "A Bradford book." |
Beschreibung: | 1 online resource (xi, 305 pages) : illustrations |
Bibliographie: | Includes bibliographical references (pages 277-300) and index. |
ISBN: | 9780262256032 0262256037 141756041X 9781417560417 9780262042192 0262042193 0262292440 9780262292443 |
Internformat
MARC
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588 | 0 | |a Print version record. | |
505 | 0 | |a Preface; Acknowledgments; 1 -- From Real to Artificial Ants; 2 -- The Ant Colony Optimization Metaheuristic; 3 -- Ant Colony Optimization Algorithms for the Traveling Salesman Problem; 4 -- Ant Colony Optimization Theory; 5 -- Ant Colony Optimization for NP-Hard Problems; 6 -- AntNet: An ACO Algorithm for Data Network Routing; 7 -- Conclusions and Prospects for the Future; Appendix: Sources of Information about the ACO Field; References; Index. | |
520 | |a An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms. | ||
546 | |a English. | ||
650 | 0 | |a Mathematical optimization. |0 http://id.loc.gov/authorities/subjects/sh85082127 | |
650 | 0 | |a Ants |x Behavior |x Mathematical models. | |
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758 | |i has work: |a ANT COLONY OPTIMIZATION (Text) |1 https://id.oclc.org/worldcat/entity/E39PD3cVX89YqJGrDTyrQ9WyHP |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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Datensatz im Suchindex
DE-BY-FWS_katkey | ZDB-4-EBA-ocm57182707 |
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adam_text | |
any_adam_object | |
author | Dorigo, Marco |
author2 | Stützle, Thomas |
author2_role | |
author2_variant | t s ts |
author_facet | Dorigo, Marco Stützle, Thomas |
author_role | |
author_sort | Dorigo, Marco |
author_variant | m d md |
building | Verbundindex |
bvnumber | localFWS |
callnumber-first | Q - Science |
callnumber-label | QA402 |
callnumber-raw | QA402.5 .D64 2004eb |
callnumber-search | QA402.5 .D64 2004eb |
callnumber-sort | QA 3402.5 D64 42004EB |
callnumber-subject | QA - Mathematics |
collection | ZDB-4-EBA |
contents | Preface; Acknowledgments; 1 -- From Real to Artificial Ants; 2 -- The Ant Colony Optimization Metaheuristic; 3 -- Ant Colony Optimization Algorithms for the Traveling Salesman Problem; 4 -- Ant Colony Optimization Theory; 5 -- Ant Colony Optimization for NP-Hard Problems; 6 -- AntNet: An ACO Algorithm for Data Network Routing; 7 -- Conclusions and Prospects for the Future; Appendix: Sources of Information about the ACO Field; References; Index. |
ctrlnum | (OCoLC)57182707 |
dewey-full | 519.6 |
dewey-hundreds | 500 - Natural sciences and mathematics |
dewey-ones | 519 - Probabilities and applied mathematics |
dewey-raw | 519.6 |
dewey-search | 519.6 |
dewey-sort | 3519.6 |
dewey-tens | 510 - Mathematics |
discipline | Mathematik |
format | Electronic eBook |
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illustrated | Illustrated |
indexdate | 2024-11-27T13:15:38Z |
institution | BVB |
isbn | 9780262256032 0262256037 141756041X 9781417560417 9780262042192 0262042193 0262292440 9780262292443 |
language | English |
oclc_num | 57182707 |
open_access_boolean | |
owner | MAIN DE-863 DE-BY-FWS |
owner_facet | MAIN DE-863 DE-BY-FWS |
physical | 1 online resource (xi, 305 pages) : illustrations |
psigel | ZDB-4-EBA |
publishDate | 2004 |
publishDateSearch | 2004 |
publishDateSort | 2004 |
publisher | MIT Press, |
record_format | marc |
spelling | Dorigo, Marco. Ant colony optimization / Marco Dorigo, Thomas Stützle. Cambridge, Mass. : MIT Press, ©2004. 1 online resource (xi, 305 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file "A Bradford book." Includes bibliographical references (pages 277-300) and index. Print version record. Preface; Acknowledgments; 1 -- From Real to Artificial Ants; 2 -- The Ant Colony Optimization Metaheuristic; 3 -- Ant Colony Optimization Algorithms for the Traveling Salesman Problem; 4 -- Ant Colony Optimization Theory; 5 -- Ant Colony Optimization for NP-Hard Problems; 6 -- AntNet: An ACO Algorithm for Data Network Routing; 7 -- Conclusions and Prospects for the Future; Appendix: Sources of Information about the ACO Field; References; Index. An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms. English. Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Ants Behavior Mathematical models. Optimisation mathématique. Fourmis Murs et comportement Modèles mathématiques. MATHEMATICS Optimization. bisacsh Ants Behavior Mathematical models fast Mathematical optimization fast COMPUTER SCIENCE/General Stützle, Thomas. has work: ANT COLONY OPTIMIZATION (Text) https://id.oclc.org/worldcat/entity/E39PD3cVX89YqJGrDTyrQ9WyHP https://id.oclc.org/worldcat/ontology/hasWork Print version: Dorigo, Marco. Ant colony optimization. Cambridge, Mass. : MIT Press, ©2004 0262042193 (DLC) 2003066629 (OCoLC)53306742 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=122502 Volltext |
spellingShingle | Dorigo, Marco Ant colony optimization / Preface; Acknowledgments; 1 -- From Real to Artificial Ants; 2 -- The Ant Colony Optimization Metaheuristic; 3 -- Ant Colony Optimization Algorithms for the Traveling Salesman Problem; 4 -- Ant Colony Optimization Theory; 5 -- Ant Colony Optimization for NP-Hard Problems; 6 -- AntNet: An ACO Algorithm for Data Network Routing; 7 -- Conclusions and Prospects for the Future; Appendix: Sources of Information about the ACO Field; References; Index. Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Ants Behavior Mathematical models. Optimisation mathématique. Fourmis Murs et comportement Modèles mathématiques. MATHEMATICS Optimization. bisacsh Ants Behavior Mathematical models fast Mathematical optimization fast |
subject_GND | http://id.loc.gov/authorities/subjects/sh85082127 |
title | Ant colony optimization / |
title_auth | Ant colony optimization / |
title_exact_search | Ant colony optimization / |
title_full | Ant colony optimization / Marco Dorigo, Thomas Stützle. |
title_fullStr | Ant colony optimization / Marco Dorigo, Thomas Stützle. |
title_full_unstemmed | Ant colony optimization / Marco Dorigo, Thomas Stützle. |
title_short | Ant colony optimization / |
title_sort | ant colony optimization |
topic | Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Ants Behavior Mathematical models. Optimisation mathématique. Fourmis Murs et comportement Modèles mathématiques. MATHEMATICS Optimization. bisacsh Ants Behavior Mathematical models fast Mathematical optimization fast |
topic_facet | Mathematical optimization. Ants Behavior Mathematical models. Optimisation mathématique. Fourmis Murs et comportement Modèles mathématiques. MATHEMATICS Optimization. Ants Behavior Mathematical models Mathematical optimization |
url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=122502 |
work_keys_str_mv | AT dorigomarco antcolonyoptimization AT stutzlethomas antcolonyoptimization |