Swarm Intelligence Algorithms A Tutorial

• Main Author: Slowik, Adam
• Format: eBook
• Language: English
• Published: United States CRC Press 2020; Taylor & Francis Group
• Edition: 1
• Subjects: Algorithms; Algorithms & Complexity; Computer Engineering; COMPUTERSCIENCEnetBASE; Data processing Computer science; ElectricalEngineeringnetBASE; ENGnetBASE; INFORMATIONSCIENCEnetBASE; Intelligent Systems; Mathematical optimization; monarch butterfly optimization; nature based optimization methods; nature inspired algorithms; SCI-TECHnetBASE; spotted hyena optimizer; STMnetBASE; Swarm intelligence; swarm intelligence algorithms; tutorial for swarm intelligence algorithms; Firefly Algorithm; Xin She; Food Source Position; ABC Algorithm; Fitness Values; Swarm Intelligence; Hunting Process; Swarm Intelligence Algorithms; Cuckoo Search Algorithm; Search Agent; Demo Codes; Global Optimization Problem; GSO; Fitness Function; Bacterial Foraging Optimization; ACO; CS; Honey Bees; Computational Creativity; Solution Vector; Onlooker Bee Phase; Matlab Code; Global Version; ACO Algorithm; BA
• ISBN: 0429749503; 9780429749506; 9780367496142; 1138384496; 0367496143; 9781138384491
• DOI: 10.1201/9780429422614

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.