Multilevel Quantum Metaheuristics - Applications in Data Exploration

Multilevel Quantum Metaheuristics: Applications in Data Exploration explores the most recent advances in hybrid quantum-inspired algorithms. Combining principles of quantum mechanics with metaheuristic techniques for efficient data optimization, this book examines multilevel quantum systems characterized by qudits and higher-level quantum states as more robust alternatives to conventional bilevel quantum approaches. It introduces novel multilevel applications of quantum metaheuristics for addressing optimization problems in areas including function optimization, data analysis, scheduling, and signal processing. The book also showcases real-world examples, case studies, and contributions that emphasize the effectiveness of proposed multilevel techniques over existing bilevel methods. Researchers, professionals, and engineers working on intelligent computing, quantum computing, data processing, clustering, and analysis, and those interested in the synergies between quantum computing, metaheuristics, and multilevel quantum systems for enhanced data exploration and analysis will find this book to be of great value.

List of contents

1. Introduction
2. A Multilevel Quantum System-based Simulated Annealing for Function Optimization
3. Entanglement-Induced Qutrits-based Multilevel Quantum Sperm Motility Algorithm for Automatic Data Clustering
4. An Optimized Qutrit-based Quantum Fuzzy Deep Neural Network for MRI Image Segmentation and Brain Tumor Classification
5. A Multilevel Quantum Bee Colony Algorithm with Social Learning Strategy and Its Application to Thresholding Image Segmentation
6. A Fuzzy Decision-based Quantum Multi-Objective Evolutionary Method for Video Summarization
7. A Multilevel Quantum Genetic Algorithm-based Feature Fusion Strategy for Human Face Identification in Information Security
8. Cancer Gene Expression Data Analysis Using Hybrid Quantum Rough Set Theoretic Clustering
9. A Qudit-inspired Differential Evolution-based Hybrid Evolutionary Technique for Air Quality Control
10. Economic Load Dispatch Strategy using a Multilevel Quantum-Inspired Ant Colony Optimization Technique with Transmission Losses
11. A Multiobjective Quantum-Inspired Gravitational Search Algorithm for Wireless Sensor Network Optimization
12. Task allocation optimization in Robotic Swarm using an Asynchronous Quantum-Inspired Elephant Herd Optimization Algorithm
13. A Quantum-Inspired Rats Herd Optimization Algorithm for Multi-objective Task Scheduling in Cloud Computing Systems 14: Conclusion

About the author

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.Hiranmoy Roy received a B.E. (CSE) degree from Burdwan University, an M. Tech. (CT) degree and PhD (Engg) from Jadavpur University, India, in 2003, 2009, and 2022, respectively. He is now with the RCC Institute of Information Technology, India, as an Associate Professor and HOD in the Department of IT. He is a lifetime member of IEI and has 21+ years of experience, including teaching. His main focus areas are heterogeneous face recognition, face recognition in the wild, generation of image descriptors, women harassment detection, human emotion recognition, image retrieval, biometric cryptography, optimization in deep learning, etc. The technologies he is dealing with are computer vision, deep learning, and image processing.
Jan Platos received a Ph.D. in computer science in 2010. He became a Full professor in 2021 at the Department of Computer Science. Since 2021, he has been Dean of the Faculty of Electrical Engineering and Computer Science, VSB-TUO. He has co-authored more than 240 scientific articles published in proceedings and journals. His primary fields of interest are machine learning, artificial intelligence, industrial data processing, text processing, data compression, bioinspired algorithms, information retrieval, data mining, data structures, and data prediction.
Leo Mršić is the Vice-Rector for science and research at Algebra University and Vice president for Technological Development at the National Council for Higher Education, Science and Technological Development, Head of BDV i-Silver Data Center Algebra LAB, Zagreb, Croatia. Permanent court expert in finance, accounting, bookkeeping, and informatics (12+ years) with a large number (150+) of successfully completed complex expertise procedures. He is also an IPMA A Certified Project Director with 100+ successfully completed complex projects.
Dr. Balamurugan Balusamy is currently working as an Associate Dean Student in Shiv Nadar Institution of Eminence, Delhi-NCR. He is part of the Top 2% Scientists Worldwide 2023 by Stanford University in the area of Data Science/AI/ML. He is also an Adjunct Professor, Department of Computer Science and Information Engineering, Taylor University, Malaysia. His contributions focus on engineering education, block chain, and data sciences.

Product details

• Assisted by: Balamurugan Balusamy (Editor), Bhattacharyya Siddhartha (Editor), Leo Mršić (Editor), Platos Jan (Editor), Hiranmoy Roy (Editor)
• Publisher: Elsevier
• Languages: English
• Product format: Paperback / Softback
• Release: 01.01.2026
• EAN: 9780443331367
• ISBN: 978-0-443-33136-7
• Series: Hybrid Computational Intelligence for Pattern Analysis and Understanding
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; Artificial Intelligence, Expert systems / knowledge-based systems, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Artificial Intelligence / Expert Systems