Swarm Intelligence Algorithms

A Tutorial
1. Auflage 2020
ISBN: 978-1-138-38449-1
Verlag: Taylor & Francis Ltd

Buch, Englisch, 362 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 454 g

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 book thoroughly presents the basics of 24 algorithms selected from the entire family of swarm intelligence algorithms. Each chapter deals with a different algorithm describing it in detail and showing how it works in the form of a pseudo-code. In addition, the source code is provided for each algorithm in Matlab and in the C ++ programming language. In order to better understand how each swarm intelligence algorithm works, a simple numerical example is included in each chapter, which guides the reader step by step through the individual stages of the algorithm, showing all necessary calculations.

This book can provide the basics for understanding how swarm intelligence algorithms work, and aid readers in programming these algorithms on their own to solve various computational problems.

This book should also be useful for undergraduate and postgraduate students studying nature-based optimization algorithms, and can be a helpful tool for learning the basics of these algorithms efficiently and quickly. In addition, it can be a useful source of knowledge for scientists working in the field of artificial intelligence, as well as for engineers interested in using this type of algorithms in their work.

If the reader already has basic knowledge of swarm intelligence algorithms, we recommend the book: "Swarm Intelligence Algorithms: Modifications and Applications" (Edited by A. Slowik, CRC Press, 2020), which describes selected modifications of these algorithms and presents their practical applications.

Zielgruppe

Academic and Professional Practice & Development

Autoren/Hrsg.

Slowik, Adam

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik
• Mathematik | Informatik Mathematik Algebra Zahlentheorie
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Mathematik | Informatik EDV | Informatik Informatik

Inhaltsverzeichnis

1 Ant Colony Optimization Pushpendra Singh, Nand K. Meena, Jin Yang, and Adam Slowik1.1 Introduction 1.2 Ants's Behavior1.3 Ant Colony Algorithm 1.4 Source-code of ACO Algorithm in Matlab1.5 Source-code of ACO Algorithm in C++ 1.6 Step-by-step numerical example of ACO algorithm1.7 Conclusion2 Articial Bee Colony AlgorithmBahriye Akay and Dervis Karaboga2.1 Introduction2.2 The Original ABC algorithm2.3 Source-code of ABC algorithm in Matlab2.4 Source-code of ABC algorithm in C++ 2.5 Step-by-step numerical example of the ABC algorithm2.6 ConclusionsReferences3 Bacterial Foraging Optimization Sonam Parashar, Nand K. Meena, Jin Yang, and Neeraj Kanwar3.1 Introduction3.2 Bacterial Foraging Optimization Algorithm3.2.1 Chemotaxis3.2.2 Swarming3.2.3 Reproduction3.2.4 Elimination and dispersal 3.3 Pseudo-code of Bacterial Foraging Optimization3.4 Matlab Source-code of Bacterial Foraging Optimization3.5 Numerical Examples3.6 Conclusions3.7 AcknowledgementReferences4 Bat Algorithm Xin-She Yang and Adam Slowik4.1 Introduction4.2 Original bat algorithm4.2.1 Description of the bat algorithm4.2.2 Pseudo-code of BA4.2.3 Parameters in the bat algorithm4.3 Source code of bat algorithm in Matlab4.4 Source code in C++ 4.5 An worked example 4.6 ConclusionReferences 5 Cat Swarm Optimization Dorin Moldovan, Viorica Chifu, Ioan Salomie, and Adam Slowik5.1 Introduction 5.2 Original CSO algorithm 5.2.1 Pseudo-code of global version of CSO algorithm 5.2.2 Description of global version of CSO algorithm 5.2.2.1 Seeking Mode (Resting)5.2.2.2 Tracing Mode (Movement) 5.2.3 Description of local version of CSO algorithm5.3 Source-code of global version of CSO algorithm in Matlab5.4 Source-code of global version of CSO algorithm in C++ 5.5 Step-by-step numerical example of global version of CSO algorithm5.6 ConclusionsReferences6 Chicken Swarm OptimizationDorin Moldovan and Adam Slowik6.1 Introduction6.2 Original CSO algorithm 6.2.1 Pseudo-code of global version of CSO algorithm6.2.2 Description of global version of CSO algorithm6.3 Source-code of global version of CSO algorithm in Matlab6.4 Source-code of global version of CSO algorithm in C++ 6.5 Step-by-step numerical example of global version of CSO algorithm6.6 ConclusionsReferences
7 Cockroach Swarm OptimizationJoanna Kwiecien7.1 Introduction7.2 Original Cockroach Swarm Optimization Algorithm 7.2.1 Pseudo-code of CSO algorithm7.2.2 Description of the CSO algorithm 7.3 Source-code of CSO algorithm in Matlab 7.4 Source-code of CSO algorithm in C++7.5 Step-by-step numerical example of CSO algorithm7.6 ConclusionsReferences 8 Crow Search AlgorithmAdam Slowik and Dorin Moldovan8.1 Introduction 8.2 Original CSA 8.3 Source-code of CSA in Matlab 8.4 Source-code of CSA in C++ 8.5 Step-by-step numerical example of CSA 8.6 Conclusions References9 Cuckoo Search Algorithm Xin-She Yang and Adam Slowik9.1 Introduction 9.2 Original Cuckoo Search9.2.1 Description of the cuckoo search9.2.2 Pseudo-code of CS9.2.3 Parameters in the cuckoo search 9.3 Source code of the cuckoo search in Matlab9.4 Source code in C++ 9.5 An worked example 9.6 Conclusion References10 Dynamic Virtual Bats AlgorithmAli Osman Topal10.1 Introduction 10.2 Dynamic Virtual Bats Algorithm 10.2.1 Pseudo-code of DVBA10.2.2 Description of DVBA10.3 Source-code of DVBA in Matlab 10.4 Source-code of DVBA in C++ 10.5 Step-by-step numerical example of DVBA 10.6 Conclusions
11 Dispersive Flies Optimisation: A Tutorial Mohammad Majid al-Rifaie11.1 Introduction 11.2 Dispersive Flies Optimisation 11.3 Source code 11.3.1 Matlab 11.3.2 C++ 11.3.3 Python 11.4 Numerical example: optimisation with DFO11.5 Conclusion References 12 Elephant Herding Optimization Nand K. Meena, Jin Yang, and Adam Slowik12.1 Introduction 12.2 Elephant Herding Optimization12.2.1 Position update of elephants in a clan 12.2.2 Separation of male elephants from the clan 12.2.3 Pseudo-code of EHO algorithm 12.3 Source-code of EHO Algorithm in Matlab 12.4 Source-code of EHO Algorithm in C++ 12.5 Step-by-step Numerical Example of EHO Algorithm 12.6 Conclusions References 13 Firey Algorithm Xi

Über Autor(innen)

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).