Modern Music-Inspired Optimization Algorithms for Electric Power Systems: Modeling, Analysis and Practice - Rilegato

Kiani-moghaddam, Mohammad; Shivaie, Mojtaba; Weinsier, Philip D.

Rilegato

ISBN 10: 3030120430 ISBN 13: 9783030120436

Casa editrice: Springer Nature, 2019

Riassunto

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.
  

Su questo libro

Informazioni sull?autore

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.

Dalla quarta di copertina

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.

Dettagli dell?edizione

• Editore: Springer Nature
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030120430
• ISBN 13: 9783030120436
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 727
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania