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.
Author(s): Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves
Series: Quantum Machine Intelligence
Publisher: CRC Press
Year: 2022
Language: English
Pages: 275
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Editors
Preface
Contributors
Chapter 1: An Introductory Illustration to Quantum-Inspired Metaheuristics
1.1. Introduction
1.2. Quantum-Inspired Metaheuristics
1.2.1. Local Search Metaheuristics
1.2.2. Constructive Metaheuristics
1.2.3. Population-based Metaheuristics
1.2.4. Hybrid Metaheuristics
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 Metaheuristics
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
2.8.3.1. The <> Bayesian Classifier
2.8.4. Quantum-Inspired Metaheuristics 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. Differences 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
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 Color 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
7.3.1. Quantum Computing
7.3.1.1. Qubit States
7.3.1.2. Qubit Operations
7.3.1.3. Qubit Measurements
7.3.2. Convolutional Neural Networks
7.3.2.1. Convolutional Layer
7.3.2.2. Pooling Layer
7.3.2.3. Fully Connected Layer
7.3.2.4. Activation Functions
7.4. Proposed Method
7.4.1. Quantum Convolutional Layer
7.4.2. Network Architecture
7.4.3. Evaluation Metrics
7.5. Results and Discussions
7.5.1. Dataset of Coronary Stenosis
7.5.2. Quantum Preprocessing
7.5.3. Training Results
7.5.4. 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
8.3.1. Elephant Herding Optimization
8.3.1.1. Clan Updation
8.3.1.2. Separation Operator
8.3.1.3. Steps of EHO
8.3.2. Basic Concepts of Quantum Computing
8.3.3. Fuzzy C Means Clustering Algorithm
8.3.4. Xie-Beni Index
8.4. Proposed Methodology
8.4.1. HSI Preprocessing
8.4.2. Qubit and Qutrit Based Elephant Herd Optimization
8.5. Experimental Results and Analysis
8.5.1. Salinas Dataset
8.5.2. Fitness Function
8.5.3. Analysis
8.6. Conlusion
Chapter 9: Toward Quantum-Inspired SSA for Solving Multiobjective Optimization Problems
9.1. Introduction
9.2. Salp Swarm Algorithm
9.2.1. Initialization
9.2.2. Leaders’ Specification
9.2.3. Updating Position
9.2.4. Re-evaluation and Decision-making
9.3. Proposed Multiobjective Quantum-inspired Salp Swarm Algorithm
9.3.1. Delta Potential-well Model for SSA
9.3.2. Salp Position Measurement
9.3.3. The New Algorithm Behavior
9.4. Experimental Procedure
9.4.1. Computing Environment
9.4.2. Performance Assessment Metrics
9.4.3. Multiobjective Benchmark Problems
9.4.4. 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
10.3. Computing the Objectives
10.3.1. CS-Measure (CSM) index
10.3.2. Davies–Bouldin (DB) Index
10.4. Multi-Objective Optimization
10.4.1. NSGA-II
10.4.2. Population Initialization and Chromosome Representation
10.4.3. Creating Cluster Centroids
10.4.4. Genetic Operation
10.4.5. Fast Non-dominated Sorting
10.4.6. Crowding Distance
10.4.7. Basic Steps of Classical NSGA-II Algorithm for Automatic Clustering of Gray Scale Images
10.5. Proposed Technique
10.5.1. Quantum State Population Initialization
10.5.2. Creating Cluster Centroids in Quantum-Inspired Framework
10.5.3. Genetic Operators in Quantum-Inspired Framework
10.5.3.1. Quantum-Behaved Selection
10.5.3.2. Quantum-Behaved Crossover
10.5.3.3. Quantum-Behaved Mutation
10.5.4. Fast Non-dominated Sorting in Quantum-Inspired Framework
10.5.5. Crowding Distance Computation in Quantum-Inspired Framework
10.5.6. QIMONSGA-II Algorithm for Automatic Clustering of Gray Scale Images
10.6. Experimental Results and Analysis
10.6.1. Used Dataset
10.6.2. Parameter Settings
10.6.3. Performance Evaluation
10.6.4. Experimental Results
10.7. Discussions and Conclusion
Chapter 11: Conclusion
Appendix A: Automatic Feature Selection for Coronary Stenosis Detection in X-Ray Angiograms
A.1. Matlab Code to Extract Vessel Segments
A.2. Matlab Code to Find Pixel Positions
A.3. Matlab Code to Extract a Window from a Matrix
A.4. Matlab Code to Find Row Vector
Index
