Swarm intelligence algorithms are a form of nature-based optimization algorithms. Their main inspiration is the cooperative behavior of animals within specific communities. This can be described as simple behaviors of individuals along with the mechanisms for sharing knowledge between them, resulting in the complex behavior of the entire community. Examples of such behavior can be found in ant colonies, bee swarms, schools of fish or bird flocks. Swarm intelligence algorithms are used to solve difficult optimization problems for which there are no exact solving methods or the use of such methods is impossible, e.g. due to unacceptable computational time.
This set comprises two volumes: Swarm Intelligence Algorithms: A Tutorial and Swarm Intelligence Algorithms: Modifications and Applications.
The first volume thoroughly presents the basics of 24 algorithms selected from the entire family of swarm intelligence algorithms. It contains a detailed explanation of how each algorithm works, along with relevant program codes in Matlab and the C ++ programming language, as well as numerical examples illustrating step-by-step how individual algorithms work.
The second volume describes selected modifications of these algorithms and presents their practical applications. This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm. It contains a short description along with a pseudo-code showing the various stages of its operation. In addition, each chapter contains a description of selected modifications of the algorithm and shows how it can be used to solve a selected practical problem.
Edited by:
Adam Slowik (Koszalin University of Technology Poland.)
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions:
Height: 234mm,
Width: 156mm,
Weight: 453g
ISBN: 9780367023454
ISBN 10: 0367023458
Pages: 768
Publication Date: 19 August 2020
Audience:
Professional and scholarly
,
Undergraduate
Format: Mixed media product
Publisher's Status: Active
Volume 1: 1. Ant Colony Optimization. 2. Artificial Bee Colony Algorithm. 3. Bacterial Foraging Optimization. 4. Bat Algorithm. 5. Cat Swarm Optimization. 6. Chicken Swarm Optimization. 7. Cockroach Swarm Optimization. 8. Crow Search Algorithm. 9. Cuckoo Search Algorithm. 10. Dynamic Virtual Bats Algorithm. 11. Dispersive Flies Optimisation: A Tutorial. 12. Elephant Herding Optimization. 13. Firey Algorithm. 14. Glowworm Swarm Optimization - A Tutorial. 15. Grasshopper Optimization Algorithm. 16. Grey Wolf Optimizer. 17. Hunting Search Algorithm. 18. Krill Herd Algorithm. 19. Monarch Buttery Optimization. 20. Particle Swarm Optimization. 21. Salp Swarm Optimization: Tutorial. 22. Social Spider Optimization. 23. Stochastic Diffusion Search: A Tutorial. 24. Whale Optimization Algorithm. Volume 2: 1. Ant Colony Optimization, Medications, and Application. 2. Artificial Bee Colony - Modications and An Application to Software Requirements Selection. 3. Modied Bacterial Forging Optimization and Application. 4. Bat Algorithm - Modications and Application. 5. Cat Swarm Optimization - Modications and Application. 6 Chicken Swarm Optimization - Modications and Application. 7 Cockroach Swarm Optimization - Modifications and Application. 8. Crow Search Algorithm - Modifications and Application. 9. Cuckoo Search Optimisation - Modifications and Application. 10. Improved Dynamic Virtual Bats Algorithm for Identifying a Suspension System Parameters. 11. Dispersive Flies Optimisation: Modifications and Application. 12. Improved Elephant Herding Optimization and Application. 13. Firey Algorithm: Variants and Applications. 14. Glowworm Swarm Optimization - Modifications and Applications. 15. Grasshopper Optimization Algorithm - Modifications and Applications. 16. Grey wolf optimizer Modifications and Applications. 17. Hunting Search Optimization Modification and Application. 18. Krill Herd Algorithm - Modifications and Applications. 19. Modified Monarch Buttery Optimization and Real-life Applications. 20. Particle Swarm Optimization - Modifications and Application. 21. Salp Swarm Algorithm: Modification and Application. 22. Social Spider Optimization - Modifications and Applications. 23. Stochastic Diffusion Search: Modifications and Application. 24 Whale Optimization Algorithm - Modifications and Applications. Modifications
Adam Slowik (IEEE Member 2007; IEEE Senior Member 2012) is an Associate Professor in the Department of Electronics and Computer Science, Koszalin University of Technology. His research interests include soft computing, computational intelligence, and, particularly, bio-inspired optimization algorithms and their engineering applications. He was a recipient of one Best Paper Award (IEEE Conference on Human System Interaction - HSI 2008).
