http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Metaheuristic optimization in power engineering
1 online resource (xiv, 517 pages) : illustrations
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.
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.
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.
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.
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.
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.
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Includes bibliographical references (pages 502-504) and index.
London, United Kingdom : The Institution of Engineering and Technology, 2018