Applications of Bat algorithm and its variants / Nilanjan Dey, V. Rajinikanth, editors.
Dey, Nilanjan, 1984-; Rajinikanth, V.
2021
QA402.5
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Title Applications of Bat algorithm and its variants / Nilanjan Dey, V. Rajinikanth, editors.
ISBN 9789811550973 (electronic bk.)
9811550972 (electronic bk.)
9811550964
9789811550966
Publication Details Singapore : Springer, [2021]
Language English
Description 1 online resource (182 pages)
Item Number 10.1007/978-981-15-5
Call Number QA402.5
Dewey Decimal Classification 519.6
Summary This book highlights essential concepts in connection with the traditional bat algorithm and its recent variants, as well as its application to find optimal solutions for a variety of real-world engineering and medical problems. Today, swarm intelligence-based meta-heuristic algorithms are extensively being used to address a wide range of real-world optimization problems due to their adaptability and robustness. Developed in 2009, the bat algorithm (BA) is one of the most successful swarm intelligence procedures, and has been used to tackle optimization tasks for more than a decade. The BAs mathematical model is quite straightforward and easy to understand and enhance, compared to other swarm approaches. Hence, it has attracted the attention of researchers who are working to find optimal solutions in a diverse range of domains, such as N-dimensional numerical optimization, constrained/unconstrained optimization and linear/nonlinear optimization problems. Along with the traditional BA, its enhanced versions are now also being used to solve optimization problems in science, engineering and medical applications around the globe.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Dey, Nilanjan, 1984-
Rajinikanth, V.
Series Springer tracts in nature-inspired computing.
Available in Other Form Applications of Bat Algorithm and Its Variants.
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Chapter 1. A New Hybrid Binary Algorithm of Bat Algorithm and Differential Evolution for Feature Selection and Classification
Chapter 2. Multi-objective Optimization of Engineering Design Problems through Pareto-Based Bat Algorithm
Chapter 3. A Study on the Bat Algorithm Technique To Evaluate The Skin Melanoma Images
Chapter 4. Multi-Thresholding with Kapurs Entropy
A Study Using Bat Algorithm with Different Search Operators
Chapter 5. Application of BAT Inspired Computing Algorithm and Its Variants In Search of Near Optimal Golomb Rulers For WDM Systems: A Comparative Study
Chapter 6. Levy Flight Opposition Embed Bat Algorithm for Model Order Reduction
Chapter 7. Application of BAT Algorithm for Detecting Malignant Brain Tumors
Chapter 8. Bat Algorithm with Applications to Signal, speech and Image Processing- A Review
Chapter 9. Bat Algorithm Aided System to Extract Tumor in Flair/T2 Modality Brain MRI Slices.

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