Fireworks Algorithm [electronic resource] : A Novel Swarm Intelligence Optimization Method / by Ying Tan.
By: Tan, Ying [author.].
Contributor(s): SpringerLink (Online service).
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BookPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2015Edition: 1st ed. 2015.Description: XXXIX, 323 p. 102 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783662463536.Subject(s): Computer science | Numerical analysis | Artificial intelligence | Computational intelligence | Robotics | Automation | Computer Science | Artificial Intelligence (incl. Robotics) | Computational Intelligence | Numeric Computing | Robotics and AutomationAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
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Springer eBooksSummary: This book is devoted to the state-of-the-art in all aspects of fireworks algorithm (FWA), with particular emphasis on the efficient improved versions of FWA. It describes the most substantial theoretical analysis including basic principle and implementation of FWA and modelling and theoretical analysis of FWA. It covers exhaustively the key recent significant research into the improvements of FWA so far. In addition, the book describes a few advanced topics in the research of FWA, including multi-objective optimization (MOO), discrete FWA (DFWA) for combinatorial optimization, and GPU-based FWA for parallel implementation. In sequels, several successful applications of FWA on non-negative matrix factorization (NMF), text clustering, pattern recognition, and seismic inversion problem, and swarm robotics, are illustrated in details, which might shed new light on more real-world applications in future. Addressing a multidisciplinary topic, it will appeal to researchers and professionals in the areas of metaheuristics, swarm intelligence, evolutionary computation, complex optimization solving, etc.
This book is devoted to the state-of-the-art in all aspects of fireworks algorithm (FWA), with particular emphasis on the efficient improved versions of FWA. It describes the most substantial theoretical analysis including basic principle and implementation of FWA and modelling and theoretical analysis of FWA. It covers exhaustively the key recent significant research into the improvements of FWA so far. In addition, the book describes a few advanced topics in the research of FWA, including multi-objective optimization (MOO), discrete FWA (DFWA) for combinatorial optimization, and GPU-based FWA for parallel implementation. In sequels, several successful applications of FWA on non-negative matrix factorization (NMF), text clustering, pattern recognition, and seismic inversion problem, and swarm robotics, are illustrated in details, which might shed new light on more real-world applications in future. Addressing a multidisciplinary topic, it will appeal to researchers and professionals in the areas of metaheuristics, swarm intelligence, evolutionary computation, complex optimization solving, etc.
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