Hybrid Quantum Metaheuristics Theory and Applications

The reference text introduces the principles of quantum mechanics to evolve hybrid metaheuristics-based optimization techniques useful for real world engineering and scientific problems. The text covers advances and trends in methodological approaches, theoretical studies, mathematical and applied techniques related to hybrid quantum metaheuristics and their applications to engineering problems. The book will be accompanied by additional resources including video demonstration for each chapter. It will be a useful text for graduate students and professional in the field of electrical engineering, electronics and communications engineering, and computer science engineering, this text: Discusses quantum mechanical principles in detail. Emphasizes the recent and upcoming hybrid quantum metaheuristics in a comprehensive manner. Provides comparative statistical test analysis with conventional hybrid metaheuristics. Highlights real-life case studies, applications, and video demonstrations.

Chapter 1 An Introductory Illustration to Quantum Inspired Meta-heuristics 1.1 Introduction 1.2 Quantum Inspired Meta-heuristics 1.2.1 Local search meta-heuristics1.2.2 Constructive meta-heuristics 1.2.3 Population-based meta-heuristics 1.2.4 Hybrid meta-heuristics 1.3 Entanglement induced optimization 1.4 W-state encoding of optimization algorithms1.5 Quantum system based optimization 1.5.1 Bi-level quantum system based optimization 1.5.2 Multi-level quantum system based optimization 1.6 Applications of Quantum Inspired Meta-heuristics1.7 Conclusion Chapter 2 A Quantum-Inspired Approach to Collective Combine Basic Classifiers 2.1 Introduction 2.2 Bagging Method 2.3 Classifiers based on similarity of objects2.4 Statistical classification algorithms 2.5 Classifiers based on class separability in attribute space 2.6 Logical classification algorithms 2.7 Neural networks2.8 Methods of combining basic classifiers 2.8.1 Voting 2.8.2 Stacking 2.8.3 Ensemble selection 2.8.4 Quantum-inspired meta-heuristics method2.9 Conclusion Chapter 3 Function Optimization using IBM Q 3.1 Introduction 3.2 Function Optimization 3.2.1 Difficulties in Optimization Methods 3.2.2 Definition of Multi-Objective Optimization Problem(MOOP) 3.2.3 Deifferences between SOOPs and MOOPs 3.3 Modern Optimization Problem-Solving Techniques 3.3.1 Genetic Algorithm 3.3.2 Simulated Annealing 3.3.3 Particle Swarm Optimization3.3.4 Bat Algorithm 3.3.5 Cuckoo Search Algorithm 3.3.6 Fuzzy System 3.3.7 Neural Network based Optimization 3.4 Quantum Computing and Optimization Algorithms 3.4.1 Quantum Computing3.4.2 Optimization using Quantum Computing 3.5 Features of IBM Q Experience 3.6 Circuit Composer IBM Q 3.7 QISKit in IBM Q 3.7.1 Creating 5-qubit circuit with the help of QISKitin IBM Q 3.7.2 Testing the circuit using IBM Quantum Computer 3.8 Optimization using IBM Q 3.9 Conclusion 3.10 Acknowledgments Chapter 4 Multipartite Adaptive Quantum-inspired Evolutionary Algorithm to Reduce Power Losses 4.1 Introduction4.2 Literature Review 4.3 Problem Formulation 4.4 Power Flow 4.5 Algorithm 4.6 Results and Discussion 4.7 Conclusions 4.8 Parameters of IEEE benchmark test bus systemChapter 5 Quantum Inspired Manta Ray Foraging Optimization Algorithm for Automatic Clustering of Colour Images 5.1 Introduction 5.2 Literature Review 5.3 Fundamentals of Quantum Computing5.3.1 Rotation Gate 5.3.2 Pauli-X Gate 5.4 Validity Measurement of Clustering 5.5 Overview of Manta Ray Foraging Optimization Algorithm 5.6 Proposed Methodology5.7 Experimental Results and Analysis 5.7.1 Developmental Entertainment 5.7.2 Dataset Used 5.7.3 Clustered Images 5.7.4 Sensitivity Analysis of QIMRFO 5.7.5 Analysis of Experimental Results5.8 Conclusion and Future scope Chapter 6 Automatic Feature Selection for Coronary Stenosis Detection in X-Ray angiograms 6.1 Introduction 6.2 Background 6.2.1 Feature Extraction.6.2.1.1 Pixel Intensity-based Features 6.2.1.2 Texture Features 6.2.1.3 Morphologic Features 6.2.2 Feature Selection 6.2.3 Support Vector Machines 6.2.4 Quantum Genetic Algorithm6.3 Proposed Method 6.4 Experiment Details 6.5 Results 6.6 Conclusion Chapter 7 Quantum Preprocessing for DCNN in Atherosclerosis Detection 7.1 Introduction7.2 Related Work 7.3 Mathematical foundations Quantum computing Convolutional Neural Networks 7.4 Proposed Method Quantum Convolutional Layer Network architecture Evaluation Metrics7.5 Results and discussions Dataset of Coronary Stenosis Quantum preprocessing Training results Detection results 7.6 Concluding remarks Chapter 8 Multilevel Quantum Elephant Herd Algorithm for AutomaticClustering of Hyperspectral Images 8.1 Introduction 8.2 Literature Survey 8.3 Background Concepts Elephant Herding Optimization Clan Updation Separation Operator Steps of EHO Basic Concepts of Quantum Computing Fuzzy C Means Clustering Algorithm Xie-Beni Index 8.4 Proposed Methodology HSI Preprocessing Qutrit Elephant Herd Optimization 8.5 Experimental Results and Analysis Salinas Dataset Fitness Function Analysis 8.6 Conlusion Chapter 9 Towards Quantum-inspired SSA for Solving Multiobjective OptimizationProblems 9.1 Introduction 9.2 Salp Swarm Algorithm Initialization Leaders Specification Updating Position Re-evaluation and Decision-making 9.3 Pproposed Multiobjective Quantum Inspired Salp SwarmAlgorithm Delta Potential-well Model for SSA Salp Position Measurement The New Algorithm Behavior 9.4 Experimental Procedure Computing Environment Performance Assessment MetricsMultiobjective Benchmark Problems Evaluating Method and Algorithms Parameters 9.5 Experiments and Discussion 9.6 Conclusion Chapter 10 Quantum Inspired Multi-Objective NSGA-II Algorithm for AutomaticClustering of Gray Scale Images 10.1 Introduction 10.2 Quantum Computing Fundamental CS-Measure (CSM) index Davies-Bouldin (DB) Index 10.4 Multi-Objective OptimizationNSGA-II Population Initialization and Chromosome Representation Creating Cluster Centroids Genetic Operation Fast Non-dominated Sorting Crowding Distance Basic Steps of Classical NSGA-II Algorithm for AutomaticClustering of Gray Scale Images 10.5 Proposed Technique Quantum State Population Initialization Creating Cluster Centroids in Quantum Inspired Framework Genetic Operators in Quantum Inspired FrameworkQuantum Behaved Selection Quantum Behaved Crossover Quantum Behaved Mutation Fast Non-dominated Sorting in Quantum Inspired Framework Crowding Distance computation in Quantum InspiredFramework QIMONSGA-II Algorithm for Automatic Clustering of Gray Scale Images 10.6 Experimental Results and Analysis Used Dataset Parameter Settings Performance Evaluation Experimental Results 10.7 Discussions and Conclusion Chapter 11 Conclusion Appendix A Automatic Feature Selection for Coronary Stenosis Detection in X-Ray angiogramsIndex

Siddhartha Bhattacharyya is currently working as a professor, department of computer science and engineering, CHRIST University Bangalore, India. His current research interest includes soft computing, pattern recognition, image processing, multimedia information retrieval, quantum-inspired soft computing, portfolio optimization and social networks. He is life fellow of Optical Society of India, Fellow of Institute for Engineering Research and Publication (IFERP) India, Institution of Electronics and Telecommunication Engineers (IETE) India, a senior member of Institute of Electrical and Electronics Engineers (IEEE) the USA and member of Institution of Engineering and Technology (IET) the UK. He has published several research papers in journals of national and international repute. Mario Köppen is presently working as a professor, graduate school of creative informatics, Kyushu Institute of Technology, Japan. He worked as a scientific assistant at the Central Institute for Cybernetics and Information Processing in Berlin and from 1992 to 2006, he was working with the Fraunhofer Institute for Production Systems and Design Technology, Germany. He has published more than 150 peer-reviewed papers in conference proceedings, journals and books and was active in the organization of various conferences as a chair or member of the program committee, including the WSC online conference series on Soft Computing in Industrial Applications, and the HIS conference series on Hybrid Intelligent Systems. He is a founding member of the World Federation of Soft Computing, and Associate Editor of the Applied Soft Computing journal. His current research interest includes image processing and pattern recognition, neural networks, evolutionary computation, fuzzy fusion, mathematical morphology, algorithm design and complexity, multi-objective and relational optimization, and digital content management. Elizabeth C. Behrman is currently working as a professor of physics and mathematics, Wichita State University, USA. She received masters and Ph.D. degrees from the University of Illinois at Urbana-Champaign, the USA in 1981 and 1985 respectively. Her research interests and publications are broad, ranging from chemical kinetics and reaction pathways to ceramic superconductors to nuclear waste vitrification. She was the first to predict the stability of inorganic Buckyball’s and buckytubes, and among the first to design and computationally test models for quantum neural networks. Her major focus interest includes theoretical quantum computing, quantum information, and quantum control, particularly quantum machine learning and quantum AI. She has taught courses including quantum mechanics, classical mechanics, solid-state physics, electricity and magnetism, computational physics, and theoretical physics. She has published several research papers in journals of national and international repute. Ivan Cruz Aceves is presently working as a researcher, Centre for Research in Mathematics (CIMAT), Mexico. He did his Ph.D. in electrical engineering from Universidad de Guanajuato, Mexico in 2014. His current research interest includes segmentation techniques and stochastic optimization methods for medical image processing and analysis. He has published research papers in the journal of national and international repute."