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Modern music-inspired optimization a...

Kiani-Moghaddam, Mohammad.

Modern music-inspired optimization algorithms for electric power systemsmodeling, analysis and practice /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modern music-inspired optimization algorithms for electric power systemsby Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier.
Reminder of title:	modeling, analysis and practice /
Author:	Kiani-Moghaddam, Mohammad.
other author:	Shivaie, Mojtaba.
Published:	Cham :Springer International Publishing :2019.
Description:	xxvii, 727 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Electric power systemsMathematical models.
Online resource:	https://doi.org/10.1007/978-3-030-12044-3
ISBN:	9783030120443$q(electronic bk.)

Modern music-inspired optimization algorithms for electric power systemsmodeling, analysis and practice /
Kiani-Moghaddam, Mohammad.

Modern music-inspired optimization algorithms for electric power systemsmodeling, analysis and practice /[electronic resource] :by Mohammad Kiani-Moghaddam, Mojtaba Shivaie, Philip D. Weinsier. - Cham :Springer International Publishing :2019. - xxvii, 727 p. :ill., digital ;24 cm. - Power systems,1612-1287. - Power systems..

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.

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.

ISBN: 9783030120443$q(electronic bk.)

Standard No.: 10.1007/978-3-030-12044-3doiSubjects--Topical Terms:

238335
Electric power systems
--Mathematical models.

LC Class. No.: TK1005 / .K536 2019

Dewey Class. No.: 621.310151

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520 $a 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.
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