Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications
- 1st Edition - July 13, 2024
- Editors: Siddhartha Bhattacharyya, Mario Köppen, Debashis De, Bijaya Ketan Panigrahi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 5 5 3 3 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 5 5 3 2 - 1
Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on recent, up-to-date technologies, combining other intelligent tools with swarm intellige… Read more
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Request a sales quoteRecent Trends in Swarm Intelligence Enabled Research for Engineering Applications focuses on recent, up-to-date technologies, combining other intelligent tools with swarm intelligence techniques to yield robust and failsafe solutions to real world problems. This book aims to provide audiences with a platform to learn and gain insights into the latest developments in hybrid swarm intelligence. It will be useful to researchers, engineers, developers, practitioners, and graduate students working in the major and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing.
With the advent of data-intensive applications, the elimination of redundancy in disseminated information has become a serious challenge for researchers who are on the lookout for evolving metaheuristic algorithms which can explore and exploit the information feature space to derive the optimal settings for specific applications. Swarm intelligence algorithms have developed as one of the most widely used metaheuristic techniques for addressing this challenge in an effective way. Inspired by the behavior of a swarm of bees, these swarm intelligence techniques emulate the corresponding natural instincts to derive optimal solutions for data-intensive applications.
- Introduces the theory underpinning hybrid swarm intelligence-enabled research as well as the leading applications across the fields of communication, networking, and information engineering
- Presents a range of applications research, including signal processing, communication engineering, bioinformatics, controllers, federated learning systems, blockchain, and IoT
- Includes case studies and code snippets in applications chapters
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Contributors
- Preface
- Chapter 1 Emerging trends in computational swarm intelligence: A comprehensive overview
- Abstract
- 1.1 Introduction and motivation
- 1.2 Computational intelligence
- 1.3 Swarm intelligence
- 1.4 Applications of swarm intelligence
- 1.5 Hybrid swarm intelligence
- 1.6 Conclusion
- References
- Chapter 2 Innovative intelligent systems and applications: A Swarm intelligence perspective
- Abstract
- 2.1 Introduction
- 2.2 Swarm behavior and general characteristics
- 2.3 SI algorithms
- 2.4 Collective sorting and clustering
- 2.5 Problem statements
- 2.6 Related works
- 2.7 Motivation
- 2.8 Future directions
- 2.9 Application of SI algorithms
- 2.10 Future challenges and trends in SI research and applications
- 2.11 Conclusion and future scope
- References
- Chapter 3 Medical image analysis using swarm intelligence: A survey
- Abstract
- 3.1 Introduction and motivation
- 3.2 Swarm intelligence techniques and literature review
- 3.3 Conclusions
- References
- Chapter 4 An optimal and robust segmentation framework for analysis and detection of brain tumor in MRI images
- Abstract
- 4.1 Introduction
- 4.2 Background
- 4.3 An optimal and robust framework for unification of FCM with ACM
- 4.4 Experimental results and discussion
- 4.5 Concluding remarks
- References
- Chapter 5 Autoimmune diseases and an approach to type 1 diabetes analysis using PSO, K-means, and silhouette values
- Abstract
- 5.1 Introduction and motivation
- 5.2 Background, definitions, and notations
- 5.3 Literature review and state of the art
- 5.4 Problem/system/application definition
- 5.5 Framework and methodology of the proposed solution
- 5.6 Experiments and results of case study 1
- 5.7 Framework and methodology: Case study 2
- 5.8 Type 1 and type 2 trend investigation in India: Case study 3
- 5.9 Enhancing clustering performance using particle swarm intelligence
- 5.10 Conclusions and future work
- References
- Chapter 6 Consumer portfolio management with usage of evolutionary approach by PSO algorithm and machine learning models
- Abstract
- 6.1 Consumer portfolio management and evolutionary algorithms
- 6.2 Literature review
- 6.3 Decision-making in complex environment conditions
- 6.4 Portfolio management solution with PSO
- 6.5 Empirical outputs
- 6.6 Discussion
- 6.7 Conclusions
- 6.8 Future research
- References
- Chapter 7 Portfolio optimization using simulated annealing and quantum-inspired simulated annealing: A comparative study
- Abstract
- 7.1 Introduction
- 7.2 Motivation and contributions
- 7.3 Overview of portfolio management
- 7.4 Related works
- 7.5 Simulated annealing
- 7.6 Overview of quantum computing
- 7.7 Quantum-inspired simulated annealing
- 7.8 Experimental results
- 7.9 Conclusion and future scope
- References
- Chapter 8 Design of vehicular ad hoc network using IoT based on particle swarm optimization technique
- Abstract
- 8.1 Introduction
- 8.2 Different optimization algorithms used in VANET
- 8.3 Survey work
- 8.4 Comparison of techniques/algorithms
- 8.5 Workflow model
- 8.6 Results
- 8.7 Conclusion
- References
- Chapter 9 Modeling and parametric optimization of grinding process using flower pollination algorithm
- Abstract
- Authors contributions
- 9.1 Introduction
- 9.2 Experimentation on grinding
- 9.3 Background
- 9.4 Proposed approach for modeling and optimization
- 9.5 Results and discussion
- 9.6 Conclusion
- References
- Chapter 10 New quantum-inspired salp swarm algorithm: A comparative study on numerical computation
- Abstract
- 10.1 Introduction and motivation
- 10.2 Background and approach
- 10.3 Proposed algorithm
- 10.4 Experimental procedure
- 10.5 Experimental study
- 10.6 Conclusion
- References
- Chapter 11 A fuzzy soft coronavirus alarm model
- Abstract
- 11.1 Introduction
- 11.2 Background study
- 11.3 Literature review
- 11.4 Model construction
- 11.5 Experimentation and results
- 11.6 Conclusion
- References
- Chapter 12 Conclusion and future research directions
- Abstract
- 12.1 Conclusion
- 12.2 Future research directions
- References
- Index
- No. of pages: 415
- Language: English
- Edition: 1
- Published: July 13, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443155338
- eBook ISBN: 9780443155321
SB
Siddhartha Bhattacharyya
MK
Mario Köppen
Mario Köppen is a professor at the Network Design and Reserach Center (NDRC) of the Kyushu Institute of Technology, where he is conducting research in the fields of multi-objective optimization, digital convergence, and multimodal content management. He studied physics at the Humboldt-University of Berlin and received his master’s degree in solid state physics in 1991. He has published around 100 peer-reviewed papers in conference proceedings, journals and books and was active in the organization of various conferences as chair or member of the program committee, including the WSC on-line conference series on Soft Computing in Industrial Applications, and the HIS conference series on Hybrid Intelligent Systems. He is founding member of the World Federation of Soft Computing, editorial board member of the Applied Soft Computing journal, the International Journal on Hybrid Intelligent Systems and the International Journal on Computational Intelligence Research.
DD
Debashis De
BP