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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Recent 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.

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

Siddhartha Bhattacharyya

Siddhartha Bhattacharyya is a Senior Researcher in the Faculty of Electrical Engineering and Computer Science of VSB Technical University of Ostrava, Czech Republic. He is also serving as the Scientific Advisor of Algebra University College, Zagreb, Croatia. Prior to this, he served as the Principal of Rajnagar Mahavidyalaya, Rajnagar, Birbhum. He was a professor at CHRIST (Deemed to be University), Bangalore, India, and also served as the Principal of RCC Institute of Information Technology, Kolkata, India. He is the recipient of several coveted national and international awards. He received the Honorary Doctorate Award (D. Litt.) from the University of South America and the SEARCC International Digital Award ICT Educator of the Year in 2017. He was appointed as the ACM Distinguished Speaker for the tenure 2018-2020. He has been appointed as the IEEE Computer Society Distinguished Visitor for the tenure 2021-2023. He has co-authored six books, co-edited 75 books, and has more than 300 research publications in international journals and conference proceedings to his credit.

Affiliations and expertise

VSB Technical University of Ostrava, Czech Republic

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.

Affiliations and expertise

Kyushu Institute of Technology, Kawazu, Iizuka-shi, Fukuoka, Japan

Debashis De

Debashis De is a professor at MAKAUT, WB, India. He is a senior member-IEEE, fellow IETE, and life member CSI. He was awarded the prestigious Boyscast Fellowship by the Department of Science and Technology, India, to work at the Heriot-Watt University, Scotland, UK. He received the Endeavour Fellowship Award from DEST Australia to work at the University of Western Australia and received the Young Scientist twice in New Delhi and in Istanbul, Turkey, from the International Union of Radio Science, Belgium. He established the Center of Mobile cloud computing (CMCC) for IoT applications, and is vice-chair of Dew Computing STC of IEEE Computer Society. Dr. De has published in 320 journals and 200 conference papers, 15 books, and filed ten patents. His research interests are in edge AI, IoT, and quantum computing.

Affiliations and expertise

Professor, Dept. of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India

Bijaya Ketan Panigrahi

Dr. B K Panigrahi is a Professor in the Electrical Engineering Department, IIT Delhi, India. Prior to joining IIT Delhi in 2005, he has served as a faculty in Electrical Engineering Department, UCE Burla, Odisha, India from 1992 to 2005. Dr Panigrahi is a senior member of IEEE and Fellow of INAE, India. His research interest includes application of soft computing and evolutionary computing techniques to power system planning, operation and control. He has also working in the field of bio-medical signal processing and image processing. He has served as the editorial board member, associate editor, and special issue guest editor of different international journals. He is also associated with various international conferences in various capacities. Dr. Panigrahi has published more than 100 research papers in various international and national journals.

Affiliations and expertise

Electrical Engineering Department, Indian Institute of Technology, Delhi, India

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Recent Trends in Swarm Intelligence Enabled Research for Engineering Applications

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Siddhartha Bhattacharyya

Mario Köppen

Debashis De

Bijaya Ketan Panigrahi