Cargando…

Swarm Intelligence Algorithms Modifications and Applications.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Slowik, Adam
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Milton : Taylor & Francis Group, 2020.
Temas:
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Dedication
  • Contents
  • Preface
  • Editor
  • Contributors
  • 1. Ant Colony Optimization, Modifications, and Application
  • 1.1 Introduction
  • 1.2 Standard ant system
  • 1.2.1 Brief of ant colony optimization
  • 1.2.2 How does the artificial ant select the edge to travel?
  • 1.2.3 Pseudo-code of standard ACO algorithm
  • 1.3 Modified variants of ant colony optimization
  • 1.3.1 Elitist ant systems
  • 1.3.2 Ant colony system
  • 1.3.3 Max-min ant system
  • 1.3.4 Rank based ant systems
  • 1.3.5 Continuous orthogonal ant systems
  • 1.4 Application of ACO to solve real-life engineering optimization problem
  • 1.4.1 Problem description
  • 1.4.2 Problem formulation
  • 1.4.3 How can ACO help to solve this optimization problem?
  • 1.4.4 Simulation results
  • 1.5 Conclusion
  • Acknowledgment
  • References
  • 2. Artificial Bee Colony
  • Modifications and An Application to Software Requirements Selection
  • 2.1 Introduction
  • 2.2 The Original ABC algorithm in brief
  • 2.3 Modifications of the ABC algorithm
  • 2.3.1 ABC with modified local search
  • 2.3.2 Combinatorial version of ABC
  • 2.3.3 Constraint handling ABC
  • 2.3.4 Multi-objective ABC
  • 2.4 Application of ABC algorithm for software requirement selection
  • 2.4.1 Problem description
  • 2.4.2 How can the ABC algorithm be used for this problem?
  • 2.4.2.1 Objective function and constraints
  • 2.4.2.2 Representation
  • 2.4.2.3 Local search
  • 2.4.2.4 Constraint handling and selection operator
  • 2.4.3 Description of the experiments
  • 2.4.4 Results obtained
  • 2.5 Conclusions
  • References
  • 3. Modified Bacterial Foraging Optimization and Application
  • 3.1 Introduction
  • 3.2 Original BFO algorithm in brief
  • 3.2.1 Chemotaxis
  • 3.2.2 Swarming
  • 3.2.3 Reproduction
  • 3.2.4 Elimination and dispersal
  • 3.2.5 Pseudo-codes of the original BFO algorithm
  • 3.3 Modifications in bacterial foraging optimization
  • 3.3.1 Non-uniform elimination-dispersal probability distribution
  • 3.3.2 Adaptive chemotaxis step
  • 3.3.3 Varying population
  • 3.4 Application of BFO for optimal DER allocation in distribution systems
  • 3.4.1 Problem description
  • 3.4.2 Individual bacteria structure for this problem
  • 3.4.3 How can the BFO algorithm be used for this problem?
  • 3.4.4 Description of experiments
  • 3.4.5 Results obtained
  • 3.5 Conclusions
  • Acknowledgement
  • References
  • 4. Bat Algorithm
  • Modifications and Application
  • 4.1 Introduction
  • 4.2 Original bat algorithm in brief
  • 4.2.1 Random fly
  • 4.2.2 Local random walk
  • 4.3 Modifications of the bat algorithm
  • 4.3.1 Improved bat algorithm
  • 4.3.2 Bat algorithm with centroid strategy
  • 4.3.3 Self-adaptive bat algorithm (SABA)
  • 4.3.4 Chaotic mapping based BA
  • 4.3.5 Self-adaptive BA with step-control and mutation mechanisms
  • 4.3.6 Adaptive position update
  • 4.3.7 Smart bat algorithm
  • 4.3.8 Adaptive weighting function and velocity