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: Modern Music-Inspired Optimization Algorithms for Electric Power Systems
    sub title: Modeling, Analysis and Practice
  name:
    name part: Kiani-Moghaddam, Mohammad.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Shivaie, Mojtaba.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Weinsier, Philip D.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXVII, 727 p. 55 illus., 40 illus. in color. online resource.
  abstract: In today's world, with an increase in the breadth and scope of real-world engineering optimization problems as well as with the advent of big data, improving the performance and efficiency of algorithms for solving such problems has become an indispensable need for specialists and researchers. In contrast to conventional books in the field that employ traditional single-stage computational, single-dimensional, and single-homogeneous optimization algorithms, this book addresses multiple newfound architectures for meta-heuristic music-inspired optimization algorithms. These proposed algorithms, with multi-stage computational, multi-dimensional, and multi-inhomogeneous structures, bring about a new direction in the architecture of meta-heuristic algorithms for solving complicated, real-world, large-scale, non-convex, non-smooth engineering optimization problems having a non-linear, mixed-integer nature with big data. The architectures of these new algorithms may also be appropriate for finding an optimal solution or a Pareto-optimal solution set with higher accuracy and speed in comparison to other optimization algorithms, when feasible regions of the solution space and/or dimensions of the optimization problem increase. This book, unlike conventional books on power systems problems that only consider simple and impractical models, deals with complicated, techno-economic, real-world, large-scale models of power systems operation and planning. Innovative applicable ideas in these models make this book a precious resource for specialists and researchers with a background in power systems operation and planning. Provides an understanding of the optimization problems and algorithms, particularly meta-heuristic optimization algorithms, found in fields such as engineering, economics, management, and operations research; Enhances existing architectures and develops innovative architectures for meta-heuristic music-inspired optimization algorithms in order to deal with complic…
  table of contents: Chapter1: Introduction to Meta-Heuristic Optimization Algorithms -- Chapter2: Introduction to Multi-Objective Optimization and Decision Making Analysis -- Chapter3: Music-Inspired Optimization Algorithms: From Past to Present -- Chapter4: Advances in Music-Inspired Optimization Algorithms -- Chapter5: Power Systems Operation -- Chapter6: Power Systems Planning -- Chapter7: Power Quality Planning.
  note: by Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier.
  subject:
    topic: Power electronics
  subject:
    topic: Mathematical optimization
  subject:
    topic: Computational intelligence
  subject:
    topic: Power Electronics, Electrical Machines and Networks
  subject:
    topic: Optimization
  subject:
    topic: Computational Intelligence
  classification: TK7881.15
  classification: 621.317
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Power Systems
  identifier: 9783030120443
  identifier: https://doi.org/10.1007/978-3-030-12044-3
  record info:
    record creation date: 190521
    record change date: 20191220130846.0
    record identifier: 978-3-030-12044-3