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.
1 Ant Colony Optimization
Pushpendra Singh, Nand K. Meena, Jin Yang, and Adam Slowik
1.1 Introduction
1.2 Ants's Behavior
1.3 Ant Colony Algorithm
1.4 Source-code of ACO Algorithm in Matlab
1.5 Source-code of ACO Algorithm in C++
1.6 Step-by-step numerical example of ACO algorithm
1.7 Conclusion
2 Arti□cial Bee Colony Algorithm
Bahriye Akay and Dervis Karaboga
2.1 Introduction
2.2 The Original ABC algorithm
2.3 Source-code of ABC algorithm in Matlab
2.4 Source-code of ABC algorithm in C++
2.5 Step-by-step numerical example of the ABC algorithm
2.6 Conclusions
References
3 Bacterial Foraging Optimization
Sonam Parashar, Nand K. Meena, Jin Yang, and Neeraj Kanwar
3.1 Introduction
3.2 Bacterial Foraging Optimization Algorithm
3.2.1 Chemotaxis
3.2.2 Swarming
3.2.3 Reproduction
3.2.4 Elimination and dispersal
3.3 Pseudo-code of Bacterial Foraging Optimization
3.4 Matlab Source-code of Bacterial Foraging Optimization
3.5 Numerical Examples
3.6 Conclusions
3.7 Acknowledgement
References
4 Bat Algorithm
Xin-She Yang and Adam Slowik
4.1 Introduction
4.2 Original bat algorithm
4.2.1 Description of the bat algorithm
4.2.2 Pseudo-code of BA
4.2.3 Parameters in the bat algorithm
4.3 Source code of bat algorithm in Matlab
4.4 Source code in C++
4.5 An worked example
4.6 Conclusion
References
5 Cat Swarm Optimization
Dorin Moldovan, Viorica Chifu, Ioan Salomie, and Adam Slowik
5.1 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 algorithm
5.3 Source-code of global version of CSO algorithm in Matlab
5.4 Source-code of global version of CSO algorithm in C++
5.5 Step-by-step numerical example of global version of CSO algorithm
5.6 Conclusions
References
6 Chicken Swarm Optimization
Dorin Moldovan and Adam Slowik
6.1 Introduction
6.2 Original CSO algorithm
6.2.1 Pseudo-code of global version of CSO algorithm
6.2.2 Description of global version of CSO algorithm
6.3 Source-code of global version of CSO algorithm in Matlab
6.4 Source-code of global version of CSO algorithm in C++
6.5 Step-by-step numerical example of global version of CSO algorithm
6.6 Conclusions
References
7 Cockroach Swarm Optimization
Joanna Kwiecien
7.1 Introduction
7.2 Original Cockroach Swarm Optimization Algorithm
7.2.1 Pseudo-code of CSO algorithm
7.2.2 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 algorithm
7.6 Conclusions
References
8 Crow Search Algorithm
Adam Slowik and Dorin Moldovan
8.1 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
References
9 Cuckoo Search Algorithm
Xin-She Yang and Adam Slowik
9.1 Introduction
9.2 Original Cuckoo Search
9.2.1 Description of the cuckoo search
9.2.2 Pseudo-code of CS
9.2.3 Parameters in the cuckoo search
9.3 Source code of the cuckoo search in Matlab
9.4 Source code in C++
9.5 An worked example
9.6 Conclusion
References
10 Dynamic Virtual Bats Algorithm
Ali Osman Topal
10.1 Introduction
10.2 Dynamic Virtual Bats Algorithm
10.2.1 Pseudo-code of DVBA
10.2.2 Description of DVBA
10.3 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-Rifaie
11.1 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 DFO
11.5 Conclusion
References
12 Elephant Herding Optimization
Nand K. Meena, Jin Yang, and Adam Slowik
12.1 Introduction
12.2 Elephant Herding Optimization
12.2.1 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 Fire□y Algorithm
Xin-She Yang and Adam Slowik
13.1 Introduction
13.2 Original □re□y algorithm
13.2.1 Description of the standard □re□y algorithm
13.2.2 Pseudo-code of FA
13.2.3 Parameters in the □re□y algorithm
13.3 Source code of □re□y algorithm in Matlab
13.4 Source code in C++
13.5 An worked example
13.6 Handling constraints
13.7 Conclusion
References
14 Glowworm Swarm Optimization - A Tutorial
Krishnanand Kaipa and Debasish Ghose
14.1 Introduction
14.1.1 Basic principle of GSO
14.1.2 The Glowworm Swarm Optimization (GSO) Algorithm
14.1.3 Algorithm description
14.2 Source-code of GSO algorithm in Matlab
14.3 Source-code of GSO algorithm in C++
14.4 Step-by-step numerical example of GSO algorithm
14.5 Conclusions
References
15 Grasshopper Optimization Algorithm
Szymon ukasik
15.1 Introduction
15.2 Description of the Grasshopper Optimization Algorithm
15.3 Source-code of GOA in Matlab
15.4 Source-code of GOA in C++
15.5 Step-by-step numerical example of GOA
15.6 Conclusion
References
16 Grey Wolf Optimizer
Ahmed F. Ali and Mohamed A. Tawhid
16.1 Introduction
16.2 Original GWO algorithm
16.2.1 Main concepts and inspiration
16.2.2 Social hierarchy
16.2.3 Encircling prey
16.2.4 Hunting process
16.2.5 Attacking prey (exploitation)
16.2.6 Search for prey (exploration)
16.2.7 Pseudo-code of GWO algorithm
16.2.8 Description of the GWO algorithm
16.3 Source-code of GWO algorithm in Matlab
16.4 Source-code of GWO algorithm in C++
16.5 Step-by-step numerical example of GWO algorithm
16.6 Conclusion
17 Hunting Search Algorithm
Ferhat Erdal and Osman Tunca
17.1 Introduction
17.2 Original HuS algorithm
17.2.1 Pseudo-code of HuS algorithm
17.2.1.1 Description of the global version of the HuS algorithm
17.3 Source code of HuS algorithm in Matlab
17.4 Source code of HuS algorithm in C++
17.5 Elaborate on HuS Algorithm with Constrained Minimization Problem
17.6 Conclusion
References
18 Krill Herd Algorithm
Ali R. Kashani, Charles V. Camp, Hamed Tohidi, and Adam Slowik
18.1 Introduction
18.2 Original KH algorithm
18.2.1 Pseudo-code of the original version of KH algorithm
18.2.2 Description of the original version of KH algorithm
18.3 Source-code of the KH algorithm in Matlab
18.4 Source-code of the KH algorithm in C++
18.5 Step-by-step numerical example of KH algorithm
18.6 Conclusion
References
19 Monarch Butter□y Optimization
Pushpendra Singh, Nand K. Meena, Jin Yang, and Adam Slowik
19.1 Introduction
19.2 Monarch Butter□y Optimization
19.2.1 Migration operator
19.2.2 Butter□y adjusting operator
19.3 Algorithm of Monarch Butter□y Optimization
19.4 Source-code of MBO Algorithm in Matlab
19.5 Source-code of MBO Algorithm in C++
19.6 Step-by-step Numerical Example of MBO Algorithm
19.7 Conclusion
References
20 Particle Swarm Optimization
Adam Slowik
20.1 Introduction
20.2 Original PSO algorithm
20.2.1 Pseudo-code of global version of PSO algorithm
20.2.2 Description of the global version of the PSO algorithm
20.2.3 Description of the local version of the PSO algorithm
20.3 Source-code of global version of PSO algorithm in Matlab
20.4 Source-code of global version of PSO algorithm in C++
20.5 Step-by-step numerical example of global version of PSO algorithm
20.6 Conclusions
References
21 Salp Swarm Optimization: Tutorial
Essam H. Houssein, Ibrahim E. Mohamed , and Aboul Ella Hassanien
21.1 Introduction
21.2 Salp Swarm Algorithm (SSA)
21.2.1 Pseudo-code of SSA algorithm
21.2.2 Description of the SSA algorithm
21.3 Source code of SSA algorithm in Matlab
21.4 Source-code of SSA algorithm in C++
21.5 Step-by-step numerical example of SSA algorithm
21.6 Conclusion
References
22 Social Spider Optimization
Ahmed F. Ali and Mohamed A. Tawhid
22.1 Introduction
22.2 Original SSO algorithm
22.2.1 Social behavior and inspiration
22.2.2 Population initialization
22.2.3 Evaluation of the solution quality
22.2.4 Modeling of the vibrations through the communal web
22.2.5 Female cooperative operator
22.2.6 Male cooperative operator
22.2.7 Mating operator
22.2.8 Pseudo-code of SSO algorithm
22.2.9 Description of the SSO algorithm
22.3 Source-code of SSO algorithm in Matlab
22.4 Source-code of SSO algorithm in C++
22.5 Step-by-step numerical example of SSO algorithm
22.6 Conclusion
References
23 Stochastic Di□usion Search: A Tutorial
Mohammad Majid al-Rifaie and J. Mark Bishop
23.1 Introduction
23.2 Stochastic Di□usion Search
23.2.1 The Mining Game
23.2.2 Re□nements in the Metaphor
23.3 SDS Architecture
23.4 Step by step example: text search
23.5 Source code
23.5.1 Matlab
23.5.2 C++
23.5.3 Python
23.6 Conclusion
References
24 Whale Optimization Algorithm
Ali R. Kashani, Charles V. Camp, Moein Armanfar, and Adam Slowik
24.1 Introduction
24.2 Original WOA
24.2.1 Pseudo-code of the WOA
24.2.2 Description of the WOA
24.3 Source-code of the WOA in Matlab
24.4 Source-code of the WOA in C++
24.5 A step-by-step numerical example of WOA
24.6 Conclusions
References
Index
Biography
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).
