Hybrid quantum metaheuristics : theory and applications /: theory and applications. (2022)

• Record Type: Book
• Further Information: Note: Edited by Siddhartha Bhattacharya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves.
• Editors: Bhattacharya, Siddhartha, 1971-; Köppen, Mario, 1964-; Behrman, Elizabeth; Cruz-Aceves, Ivan
• Contents: 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-heuristics; 1.2.2 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 algorithms; 1.5 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-heuristics; 1.7 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 objects; 2.4 Statistical classification algorithms 2.5 Classifiers based on class separability in attribute space 2.6 Logical classification algorithms 2.7 Neural networks; 2.8 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 method; 2.9 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 Optimization; 3.3.4 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 Computing; 3.4.2 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 QISKit; in 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 Introduction; 4.2 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 system; Chapter 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 Computing; 5.3.1 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 Methodology; 5.7 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 Results; 5.8 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 Algorithm; 6.3 Proposed Method 6.4 Experiment Details 6.5 Results 6.6 Conclusion Chapter 7 Quantum Preprocessing for DCNN in Atherosclerosis Detection 7.1 Introduction; 7.2 Related Work 7.3 Mathematical foundations Quantum computing Convolutional Neural Networks 7.4 Proposed Method Quantum Convolutional Layer Network architecture Evaluation Metrics; 7.5 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 Automatic; Clustering 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 Optimization; Problems 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 Swarm; Algorithm Delta Potential-well Model for SSA Salp Position Measurement The New Algorithm Behavior 9.4 Experimental Procedure Computing Environment Performance Assessment Metrics; Multiobjective 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 Automatic; Clustering of Gray Scale Images 10.1 Introduction 10.2 Quantum Computing Fundamental CS-Measure (CSM) index Davies-Bouldin (DB) Index 10.4 Multi-Objective Optimization; NSGA-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 Automatic; Clustering of Gray Scale Images 10.5 Proposed Technique Quantum State Population Initialization Creating Cluster Centroids in Quantum Inspired Framework Genetic Operators in Quantum Inspired Framework; Quantum Behaved Selection Quantum Behaved Crossover Quantum Behaved Mutation Fast Non-dominated Sorting in Quantum Inspired Framework Crowding Distance computation in Quantum Inspired; Framework 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 angiograms Index
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2022
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 670.151; Metaheuristics; Industrial engineering -- Mathematics; Engineering mathematics; Quantum systems
• Languages: English
• ISBNs: 9781000578201; 9781000578157; 9781003283294
• Related ISBNs: 9780367751562
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
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