Includes bibliographical references and index.

Online resource; title from PDF title page (Ebsco, viewed August 29, 2017).

Cover; Half-Title Page; Title Page; Copyright Page; Contents; Introduction; 1. Single Solution Based Metaheuristics; 1.1. Introduction; 1.2. The descent method; 1.3. Simulated annealing; 1.4. Microcanonical annealing; 1.5. Tabu search; 1.6. Pattern search algorithms; 1.6.1. The GRASP method; 1.6.2. Variable neighborhood search; 1.6.3. Guided local search; 1.6.4. Iterated local search; 1.7. Other methods; 1.7.1. The Nelder-Mead simplex method; 1.7.2. The noising method; 1.7.3. Smoothing methods; 1.8. Conclusion; 2. Population-based Methods; 2.1. Introduction; 2.2. Evolutionary algorithms

2.2.1. Genetic algorithms2.2.2. Evolution strategies; 2.2.3. Coevolutionary algorithms; 2.2.4. Cultural algorithms; 2.2.5. Differential evolution; 2.2.6. Biogeography-based optimization; 2.2.7. Hybrid metaheuristic based on Bayesian estimation; 2.3. Swarm intelligence; 2.3.1. Particle Swarm Optimization; 2.3.2. Ant colony optimization; 2.3.3. Cuckoo search; 2.3.4. The firefly algorithm; 2.3.5. The fireworks algorithm; 2.4. Conclusion; 3. Performance Evaluation of Metaheuristics; 3.1. Introduction; 3.2. Performance measures; 3.2.1. Quality of solutions; 3.2.2. Computational effort

3.2.3. Robustness3.3. Statistical analysis; 3.3.1. Data description; 3.3.2. Statistical tests; 3.4. Literature benchmarks; 3.4.1. Characteristics of a test function; 3.4.2. Test functions; 3.5. Conclusion; 4. Metaheuristics for FACTS Placement and Sizing; 4.1. Introduction; 4.2. FACTS devices; 4.2.1. The SVC; 4.2.2. The STATCOM; 4.2.3. The TCSC; 4.2.4. The UPFC; 4.3. The PF model and its solution; 4.3.1. The PF model; 4.3.2. Solution of the network equations; 4.3.3. FACTS implementation and network modification; 4.3.4. Formulation of FACTS placement problem as an optimization issue

4.4. PSO for FACTS placement4.4.1. Solutions coding; 4.4.2. Binary particle swarm optimization; 4.4.3. Proposed Lévy-based hybrid PSO algorithm; 4.4.4. "Hybridization" of continuous and discrete PSO algorithms for application to the positioning and sizing of FACTS; 4.5. Application to the placement and sizing of two FACTS; 4.5.1. Application to the 30-node IEEE network; 4.5.2. Application to the IEEE 57-node network; 4.5.3. Significance of the modified velocity likelihoods method; 4.5.4. Influence of the upper and lower bounds on the velocity -> Vci of particles ci

4.5.5. Optimization of the placement of several FACTS of different types (general case)4.6. Conclusion; 5. Genetic Algorithm-based Wind Farm Topology Optimization; 5.1. Introduction; 5.2. Problem statement; 5.2.1. Context; 5.2.2. Calculation of power flow in wind turbine connection cables; 5.3. Genetic algorithms and adaptation to our problem; 5.3.1. Solution encoding; 5.3.2. Selection operator; 5.3.3. Crossover; 5.3.4. Mutation; 5.4. Application; 5.4.1. Application to farms of 15-20 wind turbines; 5.4.2. Application to a farm of 30 wind turbines