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Applications of Firefly Algorithm and its Variants


Applications of Firefly Algorithm and its Variants

Case Studies and New Developments
Springer Tracts in Nature-Inspired Computing

von: Nilanjan Dey

128,39 €

Verlag: Springer
Format: PDF
Veröffentl.: 09.11.2019
ISBN/EAN: 9789811503061
Sprache: englisch

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Beschreibungen

The book discusses advantages of the firefly algorithm over other well-known metaheuristic algorithms in various engineering studies. The book provides a brief outline of various application-oriented problem solving methods, like economic emission load dispatch problem, designing a fully digital controlled reconfigurable switched beam nonconcentric ring array antenna, image segmentation, span minimization in permutation flow shop scheduling, multi-objective load dispatch problems, image compression, etc., using FA and its variants. It also covers the use of the firefly algorithm to select features, as research has shown that the firefly algorithm generates precise and optimal results in terms of time and optimality. In addition, the book also explores the potential of the firefly algorithm to provide a solution to traveling salesman problem, graph coloring problem, etc

Firefly Algorithm and its Variants in Digital Image Processing: A Comprehensive Review.- Development of firefly algorithm interface for parameter optimization of electrochemical based machining processes.- A Firefly Algorithm-based Approach for Identifying Coalitions of Energy Providers that Best Satisfy the Energy Demand.- Structural damage identification using adaptive hybride volutionary Firefly algorithm.- An automated approach for developing a Convolutional Neural Network using a modified Firefly algorithm for Image Classification.
Dr. Nilanjan Dey is an Assistant Professor in Department of Information Technology at Techno India College of Technology, Kolkata, India. He is a visiting fellow of the University of Reading, UK. He is a Visiting Professor at Wenzhou Medical University, China and Duy Tan University, Vietnam. He was an honorary Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012-2015). He was awarded his PhD from Jadavpur University in 2015. He has authored/edited more than 45 books with Elsevier, Wiley, CRC Press and Springer, and published more than 300 papers. He is the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, IGI Global, Associated Editor of IEEE Access and International Journal of Information Technology, Springer. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing, Springer, Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare, Elsevier, Series Editor of Computational Intelligence in Engineering Problem Solving and Intelligent Signal processing and data analysis, CRC. His main research interests include Medical Imaging, Machine learning, Computer Aided Diagnosis, Data Mining etc. He is the Indian Ambassador of International Federation for Information Processing – Young ICT Group. Recently, he has been awarded as one among the top 10 most published academics in the field of Computer Science in India (2015-17).
The book discusses advantages of the firefly algorithm over other well-known metaheuristic algorithms in various engineering studies. The book provides a brief outline of various application-oriented problem solving methods, like economic emission load dispatch problem, designing a fully digital controlled reconfigurable switched beam nonconcentric ring array antenna, image segmentation, span minimization in permutation flow shop scheduling, multi-objective load dispatch problems, image compression, etc., using FA and its variants. It also covers the use of the firefly algorithm to select features, as research has shown that the firefly algorithm generates precise and optimal results in terms of time and optimality. In addition, the book also explores the potential of the firefly algorithm to provide a solution to traveling salesman problem, graph coloring problem, etc
Discusses the benefits of the firefly algorithm over other methods

Provides a brief outline of firefly algorithm applications

Introduces a number of variances of the firefly algorithm

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