Swarm Intelligence Algorithms A Tutorial

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 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 Xin-She Yang and Adam Slowik13.1 Introduction 13.2 Original rey algorithm 13.2.1 Description of the standard rey algorithm 13.2.2 Pseudo-code of FA 13.2.3 Parameters in the rey algorithm 13.3 Source code of rey algorithm in Matlab13.4 Source code in C++ 13.5 An worked example13.6 Handling constraints 13.7 Conclusion References14 Glowworm Swarm Optimization - A TutorialKrishnanand Kaipa and Debasish Ghose14.1 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 AlgorithmSzymon ukasik15.1 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. Tawhid16.1 Introduction16.2 Original GWO algorithm 16.2.1 Main concepts and inspiration 16.2.2 Social hierarchy16.2.3 Encircling prey16.2.4 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 algorithm16.3 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 Tunca17.1 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 Matlab17.4 Source code of HuS algorithm in C++ 17.5 Elaborate on HuS Algorithm with Constrained Minimization Problem 17.6 Conclusion References18 Krill Herd Algorithm Ali R. Kashani, Charles V. Camp, Hamed Tohidi, and Adam Slowik18.1 Introduction 18.2 Original KH algorithm 18.2.1 Pseudo-code of the original version of KH algorithm18.2.2 Description of the original version of KH algorithm18.3 Source-code of the KH algorithm in Matlab18.4 Source-code of the KH algorithm in C++ 18.5 Step-by-step numerical example of KH algorithm18.6 Conclusion References19 Monarch Buttery Optimization Pushpendra Singh, Nand K. Meena, Jin Yang, and Adam Slowik19.1 Introduction19.2 Monarch Buttery Optimization19.2.1 Migration operator 19.2.2 Buttery adjusting operator19.3 Algorithm of Monarch Buttery Optimization19.4 Source-code of MBO Algorithm in Matlab19.5 Source-code of MBO Algorithm in C++ 19.6 Step-by-step Numerical Example of MBO Algorithm 19.7 ConclusionReferences 20 Particle Swarm OptimizationAdam Slowik20.1 Introduction 20.2 Original PSO algorithm 20.2.1 Pseudo-code of global version of PSO algorithm20.2.2 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 algorithm20.6 ConclusionsReferences21 Salp Swarm Optimization: TutorialEssam H. Houssein, Ibrahim E. Mohamed , and Aboul Ella Hassanien21.1 Introduction 21.2 Salp Swarm Algorithm (SSA) 21.2.1 Pseudo-code of SSA algorithm 21.2.2 Description of the SSA algorithm21.3 Source code of SSA algorithm in Matlab21.4 Source-code of SSA algorithm in C++ 21.5 Step-by-step numerical example of SSA algorithm21.6 Conclusion References 22 Social Spider OptimizationAhmed F. Ali and Mohamed A. Tawhid22.1 Introduction 22.2 Original SSO algorithm22.2.1 Social behavior and inspiration22.2.2 Population initialization 22.2.3 Evaluation of the solution quality22.2.4 Modeling of the vibrations through the communal web 22.2.5 Female cooperative operator22.2.6 Male cooperative operator 22.2.7 Mating o

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).