Swarm Intelligence Algorithms Modifications and Applications.
Clasificación: | Libro Electrónico |
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Autor principal: | |
Formato: | Electrónico eBook |
Idioma: | Inglés |
Publicado: |
Milton :
Taylor & Francis Group,
2020.
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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