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Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism,
artificial bee colony algorithm (ABC)  cloud model  normal cloud model  Y conditional cloud generator  global optimum  evolution  computation  urban design  biology  shape grammar  architecture  SPEA 2  energyefficient job shop scheduling  dispatching rule  nonlinear convergence factor  mutation operation  whale optimization algorithm  particle swarm optimization  confidence term  random weight  benchmark functions  ttest  success rates  average iteration times  setunion knapsack problem  moth search algorithm  transfer function  discrete algorithm  evolutionary multiobjective optimization  convergence point  acceleration search  evolutionary computation  optimization  bat algorithm (BA)  bat algorithm with multiple strategy coupling (mixBA)  CEC2013 benchmarks  Wilcoxon test  Friedman test  facility layout design  single loop  monarch butterfly optimization  slicing tree structure  material handling path  integrated design  wireless sensor networks (WSNs)  DVHop algorithm  multiobjective DVHop localization algorithm  NSGAIIDVHop  firstarrival picking  fuzzy cmeans  particle swarm optimization  range detection  minimum total dominating set  evolutionary algorithm  genetic algorithm  local search  constrained optimization problems (COPs)  evolutionary algorithms (EAs)  firefly algorithm (FA)  stochastic ranking (SR)  Artificial bee colony  swarm intelligence  elite strategy  dimension learning  global optimization  DE algorithm  ?Hilbert space  topology structure  quantum uncertainty property  numerical simulation  whale optimization algorithm  flexible job shop scheduling problem  nonlinear convergence factor  adaptive weight  variable neighborhood search  elephant herding optimization  EHO  swarm intelligence  individual updating strategy  largescale  benchmark  diversity maintenance  particle swarm optimizer  entropy  large scale optimization  minimum load coloring  memetic algorithm  evolutionary  local search  particle swarm optimization  largescale optimization  adaptive multiswarm  diversity maintenance  deep learning  convolutional neural network  rock types  automatic identification  monarch butterfly optimization  greedy optimization algorithm  global position updating operator  01 knapsack problems
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This book covers the technological progress and developments of a largescale wind energy conversion system along with its future trends, with each chapter constituting a contribution by a different leader in the wind energy arena. Recent developments in wind energy conversion systems, system optimization, stability augmentation, power smoothing, and many other fascinating topics are included in this book. Chapters are supported through modeling, control, and simulation analysis. This book contains both technical and review articles.
squirrel cage induction generator (SCIG)  doubly fed induction generator (DFIG)  fuzzy logic controller (FLC)  PI controller  low voltage ride through (LVRT)  power system  wind power forecasting  wavelet neural network  multiobjective artificial bee colony algorithm  prediction intervals  DFIGbased wind farm  transmission line  real fault cases  fault characteristics  distance protection  power smoothing  wind farm  wind turbine allocation  rotor inertia  power wind turbine  control wind turbine  battery energy storage system  wake effect  optimization  kinetic energy storage  wind farm  reliability of electricity supplies  fault diagnosis and isolation  multiple sensor faults  LPV observer  permanent magnet synchronous generator  largescale wind farm  automatic generation control  load frequency control  fractional order proportionalintegraldifferential controller  deloading  droop curve  hardwareintheloop  reserve power  primary frequency control  optimal control  wind forecast  fault current limiters  doublyfed induction generator  fault ridethrough  superconductor  series dynamic braking resistor  wind farm  Fault Ride Through (FRT)  DistributedFlexible AC Transmission system (DFACTS)  Distribution Static VAr Compensator(DSVC)  Distribution Static Synchronous Compensator (DSTATCOM)
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Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties:
3D model identification  shape normalization  weighted implicit shape representation  panoramic view  scaleinvariant feature transform  optimization  metaheuristic  parallel technique  Swarm intelligence algorithm  artificial flora (AF) algorithm  bionic intelligent algorithm  particle swarm optimization  artificial bee colony algorithm  swarm robotics  search  surveillance  behaviors  patterns  comparison  swarm behavior  Swarm Chemistry  selforganization  asymmetrical interaction  genetic algorithm  cooperative target hunting  multiAUV  improved potential field  surfacewater environment  signal source localization  multirobot system  eventtriggered communication  consensus control  timedifferenceofarrival (TDOA)  Cramer–Rao low bound (CRLB)  optimal configuration  UAV swarms  path optimization  multiple robots  formation  sliding mode controller  nonlinear disturbance observer  system stability  formation control  virtual structure  formation reconfiguration  multiagents  robotics  unmanned aerial vehicle  swarm intelligence  particle swarm optimization  search algorithm  underwater environment  sensor deployment  eventdriven coverage  fish swarm optimization  congestion control  modular robots  selfassembly robots  environmental perception  target recognition  autonomous docking  formation control  virtual linkage  virtual structure  formation reconfiguration  mobile robots  robotics  swarm robotics  formation control  coordinate motion  obstacle avoidance  n/a
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This book presents an interesting sample of the latest advances in optimization techniques applied to electrical power engineering. It covers a variety of topics from various fields, ranging from classical optimization such as Linear and Nonlinear Programming and Integer and MixedInteger Programming to the most modern methods based on bioinspired metaheuristics. The featured papers invite readers to delve further into emerging optimization techniques and their real application to case studies such as conventional and renewable energy generation, distributed generation, transport and distribution of electrical energy, electrical machines and power electronics, network optimization, intelligent systems, advances in electric mobility, etc.
Cable joint  internal defect  thermal probability density  power system optimization  optimal power flow  developed grew wolf optimizer  energy internet  prosumer  energy management  consensus  demand response  dayahead load forecasting  modular predictor  feature selection  microphasor measurement unit  mutual information theory  stochastic state estimation  twopoint estimation method  JAYA algorithm  multipopulation method (MP)  chaos optimization algorithm (COA)  economic load dispatch problem (ELD)  optimization methods  constrained parameter estimation  extended Kalman filter  power systems  C&I particle swarm optimization  ringdown detection  optimal reactive power dispatch  loss minimization  voltage deviation  hybrid method  tabu search  particle swarm optimization  artificial lighting  simulation  calibration  radiance  GenOpt  street light points  DC optimal power flow  power transfer distribution factors  generalized generation distribution factors  unit commitment  adaptive consensus algorithm  distributed heatelectricity energy management  eight searching subregions  islanded microgrid  dragonfly algorithm  metaheuristic  optimal power flow  particle swarm optimization  CCHP system  energy storage  offdesign performance  dynamic solving framework  battery energy storage system  micro grid  MILP  PCS efficiency  piecewise linear techniques  renewable energy sources  optimal operation  UC  demand bidding  demand response  genetic algorithm  load curtailment  optimization  hybrid renewable energy system  pumpedhydro energy storage  offgrid  optimization  HOMER software  rural electrification  subSaharan Africa  Cameroon  building energy management system  HVAC system  energy storage system  energy flow model  dependability  sustainability  data center  power architectures  optimization  AC/DC hybrid active distribution  hierarchical scheduling  multistakeholders  discrete wind driven optimization  multiobjective optimization  optimal power flow  metaheuristic  wind energy  photovoltaic  smart grid  transformerfault diagnosis  principal component analysis  particle swarm optimization  support vector machine  wind power  integration assessment  interactive load  considerable decomposition  controllable response  SOCP relaxations  optimal power flow  current margins  affine arithmetic  interval variables  optimizingscenarios method  power flow  wind power  active distribution system  virtual power plant  stochastic optimization  decentralized and collaborative optimization  genetic algorithm  multiobjective particle swarm optimization algorithm  artificial bee colony  IEEE Std. 802000  Schwarz’s equation  fuzzy algorithm  radial basis function  neural network  ETAP  distributed generations (DGs)  distribution network reconfiguration  runnerroot algorithm (RRA)  interturn shortedcircuit fault (ISCF)  strong track filter (STF)  linear discriminant analysis (LDA)  switched reluctance machine (SRM)  charging/discharging  electric vehicle  energy management  genetic algorithm  intelligent scatter search  electric vehicles  heterogeneous networks  demand uncertainty  power optimization  Stackelberg game  power system unit commitment  hybrid membrane computing  crossentropy  the genetic algorithm based P system  the biomimetic membrane computing  transient stability  twostage feature selection  particle encoding method  fitness function  power factor compensation  nonsinusoidal circuits  geometric algebra  evolutionary algorithms  electric power contracts  electric energy costs  cost minimization  evolutionary computation  bioinspired algorithms  congestion management  lowvoltage networks  multiobjective particle swarm optimization  affinity propagation clustering  optimal congestion threshold  optimization  magnetic field mitigation  overhead  underground  passive shielding  active shielding  MV/LV substation  n/a
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This book presents an interesting sample of the latest advances in optimization techniques applied to electrical power engineering. It covers a variety of topics from various fields, ranging from classical optimization such as Linear and Nonlinear Programming and Integer and MixedInteger Programming to the most modern methods based on bioinspired metaheuristics. The featured papers invite readers to delve further into emerging optimization techniques and their real application to case studies such as conventional and renewable energy generation, distributed generation, transport and distribution of electrical energy, electrical machines and power electronics, network optimization, intelligent systems, advances in electric mobility, etc.
Cable joint  internal defect  thermal probability density  power system optimization  optimal power flow  developed grew wolf optimizer  energy internet  prosumer  energy management  consensus  demand response  dayahead load forecasting  modular predictor  feature selection  microphasor measurement unit  mutual information theory  stochastic state estimation  twopoint estimation method  JAYA algorithm  multipopulation method (MP)  chaos optimization algorithm (COA)  economic load dispatch problem (ELD)  optimization methods  constrained parameter estimation  extended Kalman filter  power systems  C&I particle swarm optimization  ringdown detection  optimal reactive power dispatch  loss minimization  voltage deviation  hybrid method  tabu search  particle swarm optimization  artificial lighting  simulation  calibration  radiance  GenOpt  street light points  DC optimal power flow  power transfer distribution factors  generalized generation distribution factors  unit commitment  adaptive consensus algorithm  distributed heatelectricity energy management  eight searching subregions  islanded microgrid  dragonfly algorithm  metaheuristic  optimal power flow  particle swarm optimization  CCHP system  energy storage  offdesign performance  dynamic solving framework  battery energy storage system  micro grid  MILP  PCS efficiency  piecewise linear techniques  renewable energy sources  optimal operation  UC  demand bidding  demand response  genetic algorithm  load curtailment  optimization  hybrid renewable energy system  pumpedhydro energy storage  offgrid  optimization  HOMER software  rural electrification  subSaharan Africa  Cameroon  building energy management system  HVAC system  energy storage system  energy flow model  dependability  sustainability  data center  power architectures  optimization  AC/DC hybrid active distribution  hierarchical scheduling  multistakeholders  discrete wind driven optimization  multiobjective optimization  optimal power flow  metaheuristic  wind energy  photovoltaic  smart grid  transformerfault diagnosis  principal component analysis  particle swarm optimization  support vector machine  wind power  integration assessment  interactive load  considerable decomposition  controllable response  SOCP relaxations  optimal power flow  current margins  affine arithmetic  interval variables  optimizingscenarios method  power flow  wind power  active distribution system  virtual power plant  stochastic optimization  decentralized and collaborative optimization  genetic algorithm  multiobjective particle swarm optimization algorithm  artificial bee colony  IEEE Std. 802000  Schwarz’s equation  fuzzy algorithm  radial basis function  neural network  ETAP  distributed generations (DGs)  distribution network reconfiguration  runnerroot algorithm (RRA)  interturn shortedcircuit fault (ISCF)  strong track filter (STF)  linear discriminant analysis (LDA)  switched reluctance machine (SRM)  charging/discharging  electric vehicle  energy management  genetic algorithm  intelligent scatter search  electric vehicles  heterogeneous networks  demand uncertainty  power optimization  Stackelberg game  power system unit commitment  hybrid membrane computing  crossentropy  the genetic algorithm based P system  the biomimetic membrane computing  transient stability  twostage feature selection  particle encoding method  fitness function  power factor compensation  nonsinusoidal circuits  geometric algebra  evolutionary algorithms  electric power contracts  electric energy costs  cost minimization  evolutionary computation  bioinspired algorithms  congestion management  lowvoltage networks  multiobjective particle swarm optimization  affinity propagation clustering  optimal congestion threshold  optimization  magnetic field mitigation  overhead  underground  passive shielding  active shielding  MV/LV substation  n/a
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