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Nature-Inspired Optimization Algorithms

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Title: Nature-Inspired Optimization Algorithms
Description: 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
Authors: Xin-She Yang
Resource Type: eBook.
Subjects: Electronic data processing--Distributed processing, Computer algorithms, Parallel processing (Electronic computers), Artificial intelligence
Categories: COMPUTERS / Programming / Algorithms, COMPUTERS / Information Theory
Database: eBook Collection (EBSCOhost)
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  – Type: ebook-epub
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  Availability: 0
Header DbId: nlebk
DbLabel: eBook Collection (EBSCOhost)
An: 574809
RelevancyScore: 1057
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 1057.36352539063
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  Data: Nature-Inspired Optimization Algorithms
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  Data: 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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RecordInfo BibRecord:
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    Classifications:
      – Code: 006.3
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Electronic data processing--Distributed processing
        Type: general
      – SubjectFull: Computer algorithms
        Type: general
      – SubjectFull: Parallel processing (Electronic computers)
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
    Titles:
      – TitleFull: Nature-Inspired Optimization Algorithms
        Type: main
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          Name:
            NameFull: Xin-She Yang
      – PersonEntity:
          Name:
            NameFull: Xin-She Yang
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2014
            – D: 23
              M: 04
              Type: profile
              Y: 2014
          Identifiers:
            – Type: isbn-print
              Value: 9780124167438
            – Type: isbn-electronic
              Value: 9780124167452
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            – TitleFull: Nature-Inspired Optimization Algorithms
              Type: main
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