Nature-inspired optimization algorithms:
Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-cho...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
Elsevier
2014
|
| Schriftenreihe: | Elsevier insights
|
| Schlagworte: | |
| Online-Zugang: | FLA01 Volltext |
| Zusammenfassung: | Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature. Provides a theoretical understanding as well as practical implementation hints. Provides a step-by-step introduction to each algorithm |
| Beschreibung: | Includes bibliographical references |
| Beschreibung: | 1 online resource |
| ISBN: | 9780124167452 0124167454 0124167438 9780124167438 |
Internformat
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| 520 | |a Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature. Provides a theoretical understanding as well as practical implementation hints. Provides a step-by-step introduction to each algorithm | ||
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Datensatz im Suchindex
| _version_ | 1804180446327603200 |
|---|---|
| any_adam_object | |
| author | Yang, Xin-She |
| author_facet | Yang, Xin-She |
| author_role | aut |
| author_sort | Yang, Xin-She |
| author_variant | x s y xsy |
| building | Verbundindex |
| bvnumber | BV046126337 |
| collection | ZDB-33-ESD |
| ctrlnum | (ZDB-33-ESD)ocn866583452 (OCoLC)866583452 (DE-599)BVBBV046126337 |
| 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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| id | DE-604.BV046126337 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:35:53Z |
| institution | BVB |
| isbn | 9780124167452 0124167454 0124167438 9780124167438 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031506790 |
| oclc_num | 866583452 |
| open_access_boolean | |
| physical | 1 online resource |
| psigel | ZDB-33-ESD ZDB-33-ESD FLA_PDA_ESD |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Elsevier |
| record_format | marc |
| series2 | Elsevier insights |
| spelling | Yang, Xin-She Verfasser aut Nature-inspired optimization algorithms by Xin-She Yang Amsterdam Elsevier 2014 1 online resource txt rdacontent c rdamedia cr rdacarrier Elsevier insights Includes bibliographical references Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature. Provides a theoretical understanding as well as practical implementation hints. Provides a step-by-step introduction to each algorithm Algorithms fast Mathematical optimization fast Optimierung gnd Algorithmus gnd Bionik gnd Evolutionärer Algorithmus gnd Schwarmintelligenz gnd Algorithms Mathematical optimization Algorithmus (DE-588)4001183-5 gnd rswk-swf Natur (DE-588)4041358-5 gnd rswk-swf Optimierung (DE-588)4043664-0 gnd rswk-swf Algorithmus (DE-588)4001183-5 s Optimierung (DE-588)4043664-0 s Natur (DE-588)4041358-5 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 9780124167438 http://www.sciencedirect.com/science/book/9780124167438 Verlag URL des Erstveröffentlichers Volltext text file rda 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Yang, Xin-She Nature-inspired optimization algorithms Algorithms fast Mathematical optimization fast Optimierung gnd Algorithmus gnd Bionik gnd Evolutionärer Algorithmus gnd Schwarmintelligenz gnd Algorithms Mathematical optimization Algorithmus (DE-588)4001183-5 gnd Natur (DE-588)4041358-5 gnd Optimierung (DE-588)4043664-0 gnd |
| subject_GND | (DE-588)4001183-5 (DE-588)4041358-5 (DE-588)4043664-0 |
| title | Nature-inspired optimization algorithms |
| title_auth | Nature-inspired optimization algorithms |
| title_exact_search | Nature-inspired optimization algorithms |
| title_full | Nature-inspired optimization algorithms by Xin-She Yang |
| title_fullStr | Nature-inspired optimization algorithms by Xin-She Yang |
| title_full_unstemmed | Nature-inspired optimization algorithms by Xin-She Yang |
| title_short | Nature-inspired optimization algorithms |
| title_sort | nature inspired optimization algorithms |
| topic | Algorithms fast Mathematical optimization fast Optimierung gnd Algorithmus gnd Bionik gnd Evolutionärer Algorithmus gnd Schwarmintelligenz gnd Algorithms Mathematical optimization Algorithmus (DE-588)4001183-5 gnd Natur (DE-588)4041358-5 gnd Optimierung (DE-588)4043664-0 gnd |
| topic_facet | Algorithms Mathematical optimization Optimierung Algorithmus Bionik Evolutionärer Algorithmus Schwarmintelligenz Natur |
| url | http://www.sciencedirect.com/science/book/9780124167438 |
| work_keys_str_mv | AT yangxinshe natureinspiredoptimizationalgorithms |