rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Metaheuristic optimization in power engineering
  name:
    name part: Radosavljević, Jordan
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: enk
    date issued: 2018
    copyright date: 2018
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xiv, 517 pages) : illustrations
  abstract: This book describes the principles of solving various problems in power engineering via the application of selected metaheuristic optimization methods including genetic algorithms, particle swarm optimization, and the gravitational search algorithm.
  table of contents: Intro; Contents; Preface; Acknowledgements; Supplementary files; 1. Overview of metaheuristic optimization; 1.1 Introduction; 1.2 Description of metaheuristics; 1.3 Principle of population-based metaheuristics; 1.3.1 Genetic algorithm; 1.3.2 Differential evolution; 1.3.3 Evolutionary programing; 1.3.4 Backtracking search optimization algorithm; 1.3.5 Particle swarm optimization; 1.3.6 Ant colony optimization; 1.3.7 Artificial bee colony; 1.3.8 Gravitational search algorithm; 1.3.9 Wind-driven optimization; 1.3.10 Colliding bodies optimization; 1.3.11 Black hole algorithm.
  table of contents: 1.3.12 Gray wolf optimizer1.3.13 Firefly algorithm; 1.3.14 Cuckoo search algorithm; 1.3.15 Moth swarm algorithm; 1.3.16 Krill herd algorithm; 1.3.17 Shuffled frog-leaping algorithm; 1.3.18 Bacterial colony foraging optimization; 1.3.19 Biogeography-based optimization; 1.3.20 Teaching-learning-based optimization; 1.3.21 League championship algorithm; 1.3.22 Mine blast algorithm; 1.3.23 Sine cosine algorithm; 1.3.24 Harmony search; 1.3.25 Imperialist competitive algorithm; 1.3.26 Differential search algorithm; 1.3.27 Glowworm swarm optimization; 1.3.28 Spiral optimization algorithm.
  table of contents: 1.3.29 The Jaya algorithm1.3.30 Creating a ''new'' algorithm; 1.4 Criticism of metaheuristics; 1.5 Educational software-metahopt; 1.6 Conclusion; References; 2. Overview of genetic algorithms; 2.1 Introduction; 2.2 Basic structure of the GA; 2.3 Representation of individuals (encoding); 2.3.1 Binary encoding; 2.3.2 Gray coding; 2.3.3 Real-value encoding; 2.4 Population size and initial population; 2.5 Fitness function; 2.5.1 Relative fitness; 2.5.2 Linear scaling; 2.6 Selection; 2.6.1 Simple selection; 2.6.2 Stochastic universal sampling; 2.6.3 Linear ranking selection.
  table of contents: 2.6.4 Elitist selection2.6.5 k-Tournament selection schemes; 2.6.6 Simple tournament selection; 2.7 Crossover; 2.7.1 One-point crossover; 2.7.2 Multipoint crossover; 2.7.3 Uniform crossover; 2.7.4 Shuffle crossover; 2.7.5 Arithmetic crossover; 2.7.6 Heuristic crossover; 2.8 Mutation; 2.9 GA control parameters; 2.10 Multiobjective optimization using GA; 2.11 Applications of GA to power system problems-literature overview; 2.11.1 Optimal power flow; 2.11.2 Optimal reactive power dispatch; 2.11.3 Combined economic and emission dispatch; 2.11.4 Optimal power flow in distribution networks.
  table of contents: 2.11.5 Optimal placement and sizing of distributed generation in distribution networks2.11.6 Optimal energy and operation management of microgrids; 2.11.7 Optimal coordination of directional overcurrent relays; 2.11.8 Steady-state analysis of self-excited induction generator; 2.12 Conclusion; References; 3. Overview of particle swarm optimization; 3.1 Introduction; 3.2 Description of PSO; 3.2.1 Parameters of PSO; 3.2.2 General remarks about PSO; 3.2.3 MATLAB code of PSO; 3.2.4 Example usage of PSO; 3.3 PSO modifications; 3.3.1 Population topology; 3.3.2 Discrete binary PSO; 3.3.3 Hybrid PSO.
  table of contents: 3.3.4 Adaptive PSO.
  note: Jordan Radosavljević.
  note: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  note: Includes bibliographical references (pages 502-504) and index.
  subject:
    topic: Power resources
    topic: Mathematical models
  subject:
    topic: Energy industries
    topic: Mathematical models
  subject:
    topic: Mathematical optimization
  subject:
    topic: Engineering mathematics
  related item:
    title info:
      title: Metaheuristic optimization in power engineering
    name:
      name part: Radosavljević, Jordan.
    origin info:
      publisher: London, United Kingdom : The Institution of Engineering and Technology, 2018
    identifier: (OCoLC)1014157743
  related item:
    title info:
      title: IET energy engineering series ; 131
  identifier: 9781785615474
  identifier: 1785615475
  identifier: 9781523117185
  identifier: 1523117184
  identifier: 9781785615467
  identifier: 1785615467
  identifier: https://proxy.k.utb.cz/login
  access condition: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  record info:
    record content source: EBLCP
    record creation date: 180602
    record change date: 20240717213016.0
    record identifier: kn-on1038474679
    language of cataloging:
      language term: eng