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Swarm Intelligence (SI) has grown significantly, both from the perspective of algorithmic development and applications covering almost all disciplines science and technology. This book emphasizes the studies of existing SI techniques, their variants and applications. The book also contains reviews of new developments in SI techniques and hybridizations. Algorithm specific studies covering basic introduction and analysis of key components of these algorithms, such as convergence, balance of solution accuracy, computational costs, tuning and control of parameters. Application specific studies incorporating the ways of designing objective functions, solution representation and constraint handling. The book also includes studies on application domain specific adaptations in the SI techniques. The book will be beneficial for academicians and researchers from various disciplines of engineering and science working in applications of SI and other optimization problems.
Swarm Intelligence (SI) has grown significantly, both from the perspective of algorithmic development and applications covering almost all disciplines in both science and technology. Generally speaking, the algorithms that come under SI domain are typically population-based meta-heuristics techniques and are mostly inspired by nature. This book strives to cover all the major SI techniques, including particle swarm optimization, ant colony optimization, firefly algorithm, dragonfly algorithm, cuckoo search, and many more. Different variants of such SI techniques have been developed depending on applications and often hybridized with other techniques to improve performance. This also includes some of the variants and hybridized versions of popularly used SI techniques.
On the application front, SI techniques are widely used in optimization problems in different domains, including but not limited to Electrical and Power Systems, Electronics and Communication Engineering, Machine Learning, Deep Learning, Social Network Analysis, Pattern Recognition, Speech Processing, Image Processing, Bioinformatics, Health Informatics, Manufacturing and Operation Research, just to name a few. This book comprises some of the major applications of SI techniques in different domains as mentioned above. The book focuses on the adaptive nature of SI techniques from the context of applications. For specific problems, how representation of population is done, how objective function is designed, what kind of changes are done in SI technique itself, how to deal with constraints, how to manage conflicting objectives, all these issues are addressed in the book from the view point of applications.
The book is organized into four parts. Part I covers state of the art in SI, which includes reviews on SI techniques, various optimization problems, and performance analysis of SI techniques. Part II covers applications of SI techniques in various engineering problems. Part III comprises applications of SI techniques in different Machine Learning (ML) problems such as clustering and prediction. The last part includes several other applications of SI techniques.
State-of-the-Art
Engineering Problems
Machine Learning
Other Applications