E-Book, Englisch, 336 Seiten
Shukla / Tiwari Discrete Problems in Nature Inspired Algorithms
Erscheinungsjahr 2017
ISBN: 978-1-351-26086-2
Verlag: Taylor & Francis
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
E-Book, Englisch, 336 Seiten
ISBN: 978-1-351-26086-2
Verlag: Taylor & Francis
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
This book includes introduction of several algorithms which are exclusively for graph based problems, namely combinatorial optimization problems, path formation problems, etc. Each chapter includes the introduction of the basic traditional nature inspired algorithm and discussion of the modified version for discrete algorithms including problems pertaining to discussed algorithms.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Chapter 1 Introduction to Optimization Problems
1.1 Introduction
1.2 Combinatorial Optimization Problems
1.3 Graph Based Problems
1.4 Aim of this book
1.5 Chapter Summary
References
Chapter 2 Particle Swarm Optimization (PSO)
2.1 Introduction
2.2 Traditional Particle Swarm Optimization Algorithm
2.3 Variants of Particle Swarm Optimization Algorithm
2.4 Convergence Analysis of Particle Swarm Optimization Algorithm
2.5 Discrete Applications of Particle Swarm Optimization Algorithm
2.6 Search Capability of Particle Swarm Optimization Algorithm
- Quadratic Assignment Problem:-
- Chapter Summary
References
Chapter 3 Genetic Algorithm (GA)
3.1 Introduction
3.2 Encoding Schemes
3.3 Selection
3.4 Crossover
3.5 Mutation
3.6 Similarity template
3.7 Building blocks
3.8 Control parameters
3.9 Non-traditional techniques in GAS
3.10 Convergence Analysis of Genetic Algorithms
3.11 Limitations and Drawbacks of Genetic Algorithms
3.12 Chapter Summary
References
Chapter 4 Ant Colony Optimization (ACO)
4.1 Introduction
4.2 Biological Inspiration
4.3 Basic Process and Flowchart
4.4 Variants of Ant Colony Optimization
4.5 Applications
4.6 Chapter Summary
References
Chapter 5 Bat Algorithm (BA)
5.1 Introduction
5.2 Biological Inspiration
5.3 Algorithm
5.3 Related Work
References
Chapter 6 Cuckoo Search Algorithm
6.1 Introduction
6.2 Traditional Cuckoo Search Optimization Algorithm
6.3 Variations of Cuckoo Search Algorithm
6.4 Applications
6.5 Chapter Summary and Concluding Remarks
References
Chapter 7 Artificial Bee Colony
7.1 Introduction
7.2 Biological Inspiration
7.3 Swarm Behaviour
7.4 Various Stages of ABC Algorithm
7.5 Related Work
7.7 References
Chapter 8 Shuffled Frog Leap Algorithm
8.1 Introduction
8.2 Related Work Done
8.3 Travelling Salesman Problem
References
Chapter 9 Brain Storm Optimization Algorithm
9.1 Introduction
9.2 Working of Brain Storm Optimization Algorithm
9.3 Related Work in BSO and Other Contemporary Algorithms
9.4 Hybridization of BSO with PRMAlgorithm
9.5 Conclusion
9.6 Future Scope
References
Chapter 10 Intelligent Water Drop Algorithm
10.1 Intelligent Water Drop Algorithm
10.2 Intelligent Water Drop Algorithm for Discrete Applications
10.3 Variants of Intelligent Water Drop Algorithm
10.4 Scope of Intelligent Water Drop Algorithm for Numerical Analysis
10.5 Intelligent Water Drop Algorithm Exploration and Deterministic Randomness
10.6 Related Applications
References
Chapter 11 Egyptian Vulture Algorithm
11.1 Introduction
11.2 Motivation
11.3 History and Life Style of Egyptian Vulture
11.4 EGYPTIAN Vulture Optimization Algorithm
11.5 Applications of the EVOA
11.6 References
Chapter 12 Biography Based Optimization (BBO)
12.1 Introduction
12.2 Bio-geography
12.3 Bio-geography based optimization
12.4 Bio-geogrpahy based optimization Algorithm
12.5 Differnces between BBO and other population based optimization algorithm
12.6 Pseudo-code of the BBO algorithm
12.7 Application of BBO
12.8 Convergence of Biogeography-based optimization for binary problems
References
Chapter 13 Invasive Weed Optimization (IWO)
13.1 Invasive Weed Optimization
13.2 Variants of Invasive weed Optimization
13.3 Related work
13.4 Chapter Summary
References
Chapter 14 Glowworm swarm optimization
14.1 Introduction
14.2 Variants of Glowworm Swarm Optimization Algorithm
14.3 Convergence Analysis of Glowworm Swarm Optimization Algorithm
14.4 Applications of Glowworm Swarm Optimization Algorithms:
14.5 Search Capability of Glowworm Swarm Optimization Algorithm
References
Chapter 15 Bacteria Foraging Optimization Algorithm
15.1 Introduction
- Biological Inspiration
15.3 Bacterial Foraging Optimization Algorithm
15.4 Variants of BFO with Applications
References
Chapter 16 Flower Pollination Algorithm
16.1 Introduction
16.2 Flower Pollination
16.3 Characteristics of flower pollination
16.4 Flower Pollination Algorithm (FPA)
16.5 Multi-objective Flower Pollination Algorithm
16.6 Variants of Flower Pollination Algorithm
16.7 Application of Flower Pollination Algorithm
16.8 Conclusion
References
