Modern music-inspired optimization algorithms for electric power systems : modeling, analysis and practice / Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier.
Kiani-Moghaddam, Mohammad, author.; Shivaie, Mojtaba, author.; Weinsier, Philip D., author.
2019
TK1005 .K53 2019
Available Online

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Title Modern music-inspired optimization algorithms for electric power systems : modeling, analysis and practice / Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier.
Author Kiani-Moghaddam, Mohammad, author.
ISBN 9783030120443 (electronic book)
3030120449 (electronic book)
3030120430
9783030120436
DOI https://doi.org/10.1007/978-3-030-12
Published Cham, Switzerland : Springer, [2019]
Language English
Description 1 online resource.
Item Number 10.1007/978-3-030-12 doi
Call Number TK1005 .K53 2019
Dewey Decimal Classification 621.310151
Summary In todays 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 compl icated, 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.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Description based on online resource; title from digital title page (viewed on June 24, 2019).
Added Author Shivaie, Mojtaba, author.
Weinsier, Philip D., author.
Series Power systems.
Available in Other Form Modern Music-Inspired Optimization Algorithms for Electric Power Systems : Modeling, Analysis and Practice
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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.

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