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Power Systems

Modern Music-Inspired Optimization Algorithms for Electric Power Systems

Modeling, Analysis and Practice

Authors: Kiani-Moghaddam, Mohammad, Shivaie, Mojtaba, Weinsier, Philip D.

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  • 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 complicated, real-world, large-scale, non-convex, non-smooth engineering optimization problems having a non-linear, mixed-integer nature with big data
  • Addresses innovative multi-level, techno-economic, real-world, large-scale, computational-logical frameworks for power systems operation and planning, and illustrates practical training on implementation of the frameworks using the meta-heuristic music-inspired optimization algorithms
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Buy this book

eBook $219.00
price for USA (gross)
  • ISBN 978-3-030-12044-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $279.99
price for USA
  • ISBN 978-3-030-12043-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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 complicated, real-world, large-scale, non-convex, non-smooth engineering optimization problems having a non-linear, mixed-integer nature with big data;
  • Addresses innovative multi-level, techno-economic, real-world, large-scale, computational-logical frameworks for power systems operation and planning, and illustrates practical training on implementation of the frameworks using the meta-heuristic music-inspired optimization algorithms.


About the authors

Mohammad Kiani-Moghaddam received the B.Sc. degree with first class honors in Electrical Engineering from the Islamic Azad University of Najafabad, Isfahan, Iran, and the M.Sc. degree with first class honors in Electrical Engineering from the Shahid Beheshti University, Tehran, Iran. His emphasis is on the research, design, and application of complex mathematical models for use in the analysis of power systems with a particular focus on risk assessment, worth-based reliability evaluation, economic strategies, as well as artificial intelligence and optimization theory. He has served as a peer reviewer for over four international journals.
Mojtaba Shivaie is currently an Assistant Professor in the Faculty of Electrical Engineering and Robotic at the Shahrood University of Technology, Shahrood, Iran. He obtained the B.Sc. degree with first class honors in Electrical Engineering from the Semnan University, Semnan, Iran, in 2008. He also received the M.Sc. and Ph.D. degrees with first class honors, both in Electrical Engineering, from the Shahid Beheshti University, Tehran, Iran, in 2010 and 2015, respectively. He has worked extensively in the areas of power systems, smart distribution grids, stochastic simulation and optimization techniques, and he (with Mr. Kiani-Moghaddam and Prof. Weinsier) is the inventor of a modern optimization technique known as “symphony orchestra search algorithm” and an innovative architecture for competitive electricity markets known as “Hypaethral market”. He was awarded the Dr. Shahriari’s scholarship by the office of honor students of the Shahid Beheshti University and the Dr. Kazemi-Ashtiani’s award by the Iran’s National Elites Foundation for outstanding educational and research achievements. He has served as an editorial board of the International Transaction of Electrical and Computer Engineers System journal and the Control and Systems Engineering journal and also a peer reviewer for over twelve high impact journals. He was a recipient of the outstanding reviewer award of the Applied Soft Computing in 2014, the Energy Conversion and Management in 2016, and the Electric Power Systems Research in 2017.
Philip D. Weinsier is currently Professor and Electrical/Electronic Engineering Technology Program Director at Bowling Green State University-Firelands. He received his BS degrees in Physics/Mathematics and Industrial Education/Teaching from Berry College in 1978; MS degree in Industrial Education and EdD degree in Vocational/Technical Education from Clemson University in 1979 and 1990, respectively. He is currently senior editor of the International Journal of Modern Engineering and the International Journal of Engineering Research and Innovation, and Editor-in-Chief of the Technology Interface International Journal. He is a Fulbright Scholar, a lifetime member of the International Fulbright Association, and a member of the European Association for Research on Learning and Instruction since 1989.

Table of contents (7 chapters)

Table of contents (7 chapters)
  • Introduction to Meta-heuristic Optimization Algorithms

    Pages 3-20

    Kiani-Moghaddam, Mohammad (et al.)

  • Introduction to Multi-objective Optimization and Decision-Making Analysis

    Pages 21-45

    Kiani-Moghaddam, Mohammad (et al.)

  • Music-Inspired Optimization Algorithms: From Past to Present

    Pages 47-95

    Kiani-Moghaddam, Mohammad (et al.)

  • Advances in Music-Inspired Optimization Algorithms

    Pages 97-262

    Kiani-Moghaddam, Mohammad (et al.)

  • Power Systems Operation

    Pages 265-325

    Kiani-Moghaddam, Mohammad (et al.)

Buy this book

eBook $219.00
price for USA (gross)
  • ISBN 978-3-030-12044-3
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $279.99
price for USA
  • ISBN 978-3-030-12043-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Modern Music-Inspired Optimization Algorithms for Electric Power Systems
Book Subtitle
Modeling, Analysis and Practice
Authors
Series Title
Power Systems
Copyright
2019
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-12044-3
DOI
10.1007/978-3-030-12044-3
Hardcover ISBN
978-3-030-12043-6
Series ISSN
1612-1287
Edition Number
1
Number of Pages
XXVII, 727
Number of Illustrations
15 b/w illustrations, 40 illustrations in colour
Topics