Metaheuristic optimization in power engineering

This book describes the principles of solving various problems in power engineering via the application of selected metaheuristic optimization methods including genetic algorithms, particle swarm optimization, and the gravitational search algorithm.

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Bibliographic Details
Main Author: Radosavljević, Jordan, (Author)
Format: eBook
Language: English
Published: London, United Kingdom : The Institution of Engineering and Technology, 2018.
Series: IET energy engineering series ; 131.
Subjects:
Power resources > Mathematical models.
Energy industries > Mathematical models.
Mathematical optimization.
Engineering mathematics.
elektronické knihy
electronic books
ISBN: 9781785615474
1785615475
9781523117185
1523117184
9781785615467
1785615467
Physical Description: 1 online resource (xiv, 517 pages) : illustrations

Cover

Table of contents

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035 |a (OCoLC)1038474679  |z (OCoLC)1037060299  |z (OCoLC)1057434350 
100 1 |a Radosavljević, Jordan,  |e author. 
245 1 0 |a Metaheuristic optimization in power engineering /  |c Jordan Radosavljević. 
264 1 |a London, United Kingdom :  |b The Institution of Engineering and Technology,  |c 2018. 
264 4 |c ©2018 
300 |a 1 online resource (xiv, 517 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a IET energy engineering series ;  |v 131 
504 |a Includes bibliographical references (pages 502-504) and index. 
505 0 |a Intro; Contents; Preface; Acknowledgements; Supplementary files; 1. Overview of metaheuristic optimization; 1.1 Introduction; 1.2 Description of metaheuristics; 1.3 Principle of population-based metaheuristics; 1.3.1 Genetic algorithm; 1.3.2 Differential evolution; 1.3.3 Evolutionary programing; 1.3.4 Backtracking search optimization algorithm; 1.3.5 Particle swarm optimization; 1.3.6 Ant colony optimization; 1.3.7 Artificial bee colony; 1.3.8 Gravitational search algorithm; 1.3.9 Wind-driven optimization; 1.3.10 Colliding bodies optimization; 1.3.11 Black hole algorithm. 
505 8 |a 1.3.12 Gray wolf optimizer1.3.13 Firefly algorithm; 1.3.14 Cuckoo search algorithm; 1.3.15 Moth swarm algorithm; 1.3.16 Krill herd algorithm; 1.3.17 Shuffled frog-leaping algorithm; 1.3.18 Bacterial colony foraging optimization; 1.3.19 Biogeography-based optimization; 1.3.20 Teaching-learning-based optimization; 1.3.21 League championship algorithm; 1.3.22 Mine blast algorithm; 1.3.23 Sine cosine algorithm; 1.3.24 Harmony search; 1.3.25 Imperialist competitive algorithm; 1.3.26 Differential search algorithm; 1.3.27 Glowworm swarm optimization; 1.3.28 Spiral optimization algorithm. 
505 8 |a 1.3.29 The Jaya algorithm1.3.30 Creating a ''new'' algorithm; 1.4 Criticism of metaheuristics; 1.5 Educational software-metahopt; 1.6 Conclusion; References; 2. Overview of genetic algorithms; 2.1 Introduction; 2.2 Basic structure of the GA; 2.3 Representation of individuals (encoding); 2.3.1 Binary encoding; 2.3.2 Gray coding; 2.3.3 Real-value encoding; 2.4 Population size and initial population; 2.5 Fitness function; 2.5.1 Relative fitness; 2.5.2 Linear scaling; 2.6 Selection; 2.6.1 Simple selection; 2.6.2 Stochastic universal sampling; 2.6.3 Linear ranking selection. 
505 8 |a 2.6.4 Elitist selection2.6.5 k-Tournament selection schemes; 2.6.6 Simple tournament selection; 2.7 Crossover; 2.7.1 One-point crossover; 2.7.2 Multipoint crossover; 2.7.3 Uniform crossover; 2.7.4 Shuffle crossover; 2.7.5 Arithmetic crossover; 2.7.6 Heuristic crossover; 2.8 Mutation; 2.9 GA control parameters; 2.10 Multiobjective optimization using GA; 2.11 Applications of GA to power system problems-literature overview; 2.11.1 Optimal power flow; 2.11.2 Optimal reactive power dispatch; 2.11.3 Combined economic and emission dispatch; 2.11.4 Optimal power flow in distribution networks. 
505 8 |a 2.11.5 Optimal placement and sizing of distributed generation in distribution networks2.11.6 Optimal energy and operation management of microgrids; 2.11.7 Optimal coordination of directional overcurrent relays; 2.11.8 Steady-state analysis of self-excited induction generator; 2.12 Conclusion; References; 3. Overview of particle swarm optimization; 3.1 Introduction; 3.2 Description of PSO; 3.2.1 Parameters of PSO; 3.2.2 General remarks about PSO; 3.2.3 MATLAB code of PSO; 3.2.4 Example usage of PSO; 3.3 PSO modifications; 3.3.1 Population topology; 3.3.2 Discrete binary PSO; 3.3.3 Hybrid PSO. 
505 8 |a 3.3.4 Adaptive PSO. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a This book describes the principles of solving various problems in power engineering via the application of selected metaheuristic optimization methods including genetic algorithms, particle swarm optimization, and the gravitational search algorithm. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Power resources  |x Mathematical models. 
650 0 |a Energy industries  |x Mathematical models. 
650 0 |a Mathematical optimization. 
650 0 |a Engineering mathematics. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |i Print version:  |a Radosavljević, Jordan.  |t Metaheuristic optimization in power engineering.  |d London, United Kingdom : The Institution of Engineering and Technology, 2018  |z 1785615467  |w (OCoLC)1014157743 
830 0 |a IET energy engineering series ;  |v 131. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpMOPE0002/metaheuristic-optimization-in?kpromoter=marc  |y Full text 

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