ABSTRACT

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.

chapter 1|15 pages

Swarm Intelligence Algorithms: A Tutorial

ByPushpendra Singh, Nand K. Meena, Jin Yang, Adam Slowik

chapter 2|14 pages

Artificial Bee Colony Algorithm

ByBahriye Akay, Dervis Karaboga

chapter 3|12 pages

Bacterial Foraging Optimization

BySonam Parashar, Nand K. Meena, Jin Yang, Neeraj Kanwar

chapter 4|11 pages

Bat Algorithm

ByXin-She Yang, Adam Slowik

chapter 5|15 pages

Cat Swarm Optimization

ByDorin Moldovan, Viorica Chifu, Ioan Salomie, Adam Slowik

chapter 6|13 pages

Chicken Swarm Optimization

ByDorin Moldovan, Adam Slowik

chapter 7|12 pages

Cockroach Swarm Optimization

ByJoanna Kwiecien

chapter 8|12 pages

Crow Search Algorithm

ByAdam Slowik, Dorin Moldovan

chapter 9|12 pages

Cuckoo Search Algorithm

ByXin-She Yang, Adam Slowik

chapter 10|14 pages

Dynamic Virtual Bats Algorithm

ByTopal Ali Osman

chapter 11|13 pages

Dispersive Flies Optimisation: A Tutorial

ByMohammad Majid-al-Rifaie

chapter 12|14 pages

Elephant Herding Optimization

ByNand K. Meena, Jin Yang, Adam Slowik

chapter 13|12 pages

Firefly Algorithm

ByXin-She Yang, Adam Slowik

chapter 14|17 pages

Glowworm Swarm Optimization: A Tutorial

ByKrishnanand Kaipa, Debasish Ghose

chapter 15|13 pages

Grasshopper Optimization Algorithm

BySzymon Łukasik

chapter 16|12 pages

Grey Wolf Optimizer

ByAhmed F. Ali, Mohamed A. Tawhid

chapter 17|12 pages

Hunting Search Algorithm

ByFerhat Erdal, Osman Tunca

chapter 18|18 pages

Krill Herd Algorithm

ByAli R. Kashani, Charles V. Camp, Hamed Tohidi, Adam Slowik

chapter 19|15 pages

Monarch Butterfly Optimization

ByPushpendra Singh, Nand K. Meena, Jin Yang, Adam Slowik

chapter 20|13 pages

Particle Swarm Optimization

ByAdam Slowik

chapter 21|14 pages

Salp Swarm Algorithm: Tutorial

ByEssam H. Houssein, Ibrahim E. Mohamed, Aboul Ella Hassanien

chapter 22|13 pages

Social spider optimization

ByAhmed F. Ali, Mohamed A. Tawhid

chapter 23|15 pages

Stochastic Diffusion Search: A Tutorial

ByMohammad Majid-al-Rifaie, J. Mark Bishop

chapter 24|11 pages

Whale Optimization Algorithm

ByAli R. Kashani, Charles V. Camp, Moein Armanfar, Adam Slowik