| Formatted contents note |
1 Gaussian Quantum-Behaved Particle Swarm Optimization<br/>Applied to Fuzzy FID Controller Design<br/>Leandro dos Santos Coelho, Nadia Nedjah, Luiza de Macedo Mourelle<br/>1.1 Introduction<br/>1.2 Fuzzy Control Algorithm .<br/>1.2.1 Fuzzy PID controller<br/>1.3 Quantum-behaved Particle Swarm Optimization<br/>1.3.1 QPSO using Gaussian distribution<br/>1.4 Case Study and Simulation Results<br/>1.4.1 Description of Control Valve<br/>1.4.2 Simulation results<br/>1.5 Conclusion and Future Work<br/>2 Quantum-inspired genetic algorithms for flow shop<br/>scheduling<br/>Ling Wang^ Bin-bin Li.<br/>2.1 Introduction<br/>2.2 Permutation flow shop scheduling<br/>2.2.1 Problem statement<br/>2.2.2 GA-based multi-objective PFSS .<br/>2.3 Quantum-inspired genetic algorithm<br/>2.4 Hybrid QGA for single-objective PFSS<br/>2.4.1 QGA for single-objective PFSS<br/>2.4.2 Permutation-based GA<br/>2.4.3 Hybrid QGA for PFSS<br/>2.5 Hybrid QGA for multi-objective FSSP<br/>2.5.1 Solution Evaluation in Multi-objective Sense<br/>2.5.2 Population Trimming in QGA and PGA<br/>2.5.3 Genetic Operators and Stopping Criteria in QGA and PGA .<br/>2.5.4 Population Upadting in QGA and PGA<br/>VI Contents<br/>2.5.5 Procedure of QGA and PGA<br/>2.5.6 Hybrid Quantum-Inspired Genetic Algorithm<br/>2.6 Performance metrics of multi-objective optimization<br/>2.6.1 Overall Non-dominated Vector Generation {ONVG)<br/>2.6.2 C Metric (CM)<br/>2.6.3 Distance iMetrics {Dav and Dmax)<br/>2.6.4 Tan's Spacing (TS)<br/>2.6.5 Maximum Spread {MS)<br/>2.6.6 Average Quality {AQ)<br/>2.7 Simulation results and comparisons on single-objective PFFS<br/>2.8 Simulation results and comparisons on multi-objective PFFS<br/>2.8.1 Testing on existing problems in the literature<br/>2.8.2 Testing and comparisons on randomly generated instances<br/>2.9 Conclusions and future work<br/>]<br/>3 Quantum Simulataneous Recurrent Networks for Content<br/>Addressable Memory<br/>Raheel Allauddin, Stuart Boehmer, Elizabeth C. Behrman, Kavitha<br/>Gaddam, James E. Steak<br/>3.1 Introduction<br/>3.2 The Hopfield Model<br/>3.3 THE QUANTUM SYSTEM<br/>3.4 Mapping the Quantum System onto the Hopheld iNet<br/>3.5 QHN As An Information Propagator for a Microtubulin Architecture<br/>3.6 QHN As Simultaneous Recurrent Network<br/>3.7 Application: The CNOT Gate<br/>4 Quantum Intelligent Mobile System<br/>Chunlin Chen, Daoyi Dong<br/>4.1 Introduction<br/>4.2 Prerequisite<br/>4.2.1 Intelligent mobile system<br/>4.2.2 Fundamentals of quantum mechanics<br/>4.3 Architecture of QIMS based on multi-quantum-agent<br/>4.3.1 Intelligent agent in quantum system<br/>4.3.2 Multi-quantum-agent system<br/>4.3.3 Architecture of QIMS based on MQAS<br/>4.4 Hardware of typical QIMS<br/>4.4.1 Quantum computers<br/>4.4.2 Classical-quantum hybrid components<br/>4.5 Planning for QIMS based on Grover algorithm<br/>4.5.1 A general framework of planning tasks .<br/>4.5.2 Grover algorithm<br/>4.5.3 Planning using Grover algorithm<br/>4.6 Learning for QIMS using QRL<br/>4.6.1 QRL<br/>4.6.2 Learning in unknown environment using QRL<br/>4.7 Discussion or ^he applications of QIMS<br/>4.8 Conclusions<br/>5 Quantum Associative Pattern Retrieval<br/>Carlo A. Trugenberger, Cristina M. Diamantini.<br/>5.1 Introduction<br/>5.2 Quantizing Neural Networks<br/>5.3 Quantum Associative Memories<br/>5.4 Phase Structure<br/>5.5 Summary<br/>6 Quantum-Inspired Evolutionary Algorithm for Numerical<br/>Optimization<br/>Andre Vargas Aba da Cruz, Marley M. B. R. Vellasco, Marco Aurelio<br/>C. Pacheco<br/>6.1 Introduction<br/>6.2 The Quantum-Inspired Evolutionary Algorithm using a Real<br/>Number Representation<br/>6.2.1 The Quantum Population<br/>6.2.2 Quantum Individuals Observation<br/>6.2.3 Updating the Quantum Population<br/>6.3 Case Studies<br/>6.3.1 Optimization of Benchmark Functions<br/>6.3.2 Discussion<br/>6.4 Conclusions and Future Works<br/>7 Calibration of the VGSSD Option Pricing Model<br/>using a Quantum-inspired Evolutionary Algorithm<br/>Kai Fan, Conall O'Sullivan, Anthony Brabazon, Michael O'Neill, Sean<br/>McCarraghy<br/>7.1 Introduction<br/>7.2 The Quantum-inspired Genetic Algorithm<br/>7.2.1 Representing a Quantum System<br/>7.2.2 Real-valued quantum-inspired evolutionary algorithms.<br/>7.3 Option Pricing Model Calibration<br/>7.4 Experimental Approach<br/>7.5 Results<br/>7.6 Conclusions1 Gaussian Quantum-Behaved Particle Swarm Optimization<br/>Applied to Fuzzy FID Controller Design<br/>Leandro dos Santos Coelho, Nadia Nedjah, Luiza de Macedo Mourelle<br/>1.1 Introduction<br/>1.2 Fuzzy Control Algorithm .<br/>1.2.1 Fuzzy PID controller<br/>1.3 Quantum-behaved Particle Swarm Optimization<br/>1.3.1 QPSO using Gaussian distribution<br/>1.4 Case Study and Simulation Results<br/>1.4.1 Description of Control Valve<br/>1.4.2 Simulation results<br/>1.5 Conclusion and Future Work<br/>2 Quantum-inspired genetic algorithms for flow shop<br/>scheduling<br/>Ling Wang^ Bin-bin Li.<br/>2.1 Introduction<br/>2.2 Permutation flow shop scheduling<br/>2.2.1 Problem statement<br/>2.2.2 GA-based multi-objective PFSS .<br/>2.3 Quantum-inspired genetic algorithm<br/>2.4 Hybrid QGA for single-objective PFSS<br/>2.4.1 QGA for single-objective PFSS<br/>2.4.2 Permutation-based GA<br/>2.4.3 Hybrid QGA for PFSS<br/>2.5 Hybrid QGA for multi-objective FSSP<br/>2.5.1 Solution Evaluation in Multi-objective Sense<br/>2.5.2 Population Trimming in QGA and PGA<br/>2.5.3 Genetic Operators and Stopping Criteria in QGA and PGA .<br/>2.5.4 Population Upadting in QGA and PGA<br/>VI Contents<br/>2.5.5 Procedure of QGA and PGA<br/>2.5.6 Hybrid Quantum-Inspired Genetic Algorithm<br/>2.6 Performance metrics of multi-objective optimization<br/>2.6.1 Overall Non-dominated Vector Generation {ONVG)<br/>2.6.2 C Metric (CM)<br/>2.6.3 Distance iMetrics {Dav and Dmax)<br/>2.6.4 Tan's Spacing (TS)<br/>2.6.5 Maximum Spread {MS)<br/>2.6.6 Average Quality {AQ)<br/>2.7 Simulation results and comparisons on single-objective PFFS<br/>2.8 Simulation results and comparisons on multi-objective PFFS<br/>2.8.1 Testing on existing problems in the literature<br/>2.8.2 Testing and comparisons on randomly generated instances<br/>2.9 Conclusions and future work<br/>]<br/>3 Quantum Simulataneous Recurrent Networks for Content<br/>Addressable Memory<br/>Raheel Allauddin, Stuart Boehmer, Elizabeth C. Behrman, Kavitha<br/>Gaddam, James E. Steak<br/>3.1 Introduction<br/>3.2 The Hopfield Model<br/>3.3 THE QUANTUM SYSTEM<br/>3.4 Mapping the Quantum System onto the Hopheld iNet<br/>3.5 QHN As An Information Propagator for a Microtubulin Architecture<br/>3.6 QHN As Simultaneous Recurrent Network<br/>3.7 Application: The CNOT Gate<br/>4 Quantum Intelligent Mobile System<br/>Chunlin Chen, Daoyi Dong<br/>4.1 Introduction<br/>4.2 Prerequisite<br/>4.2.1 Intelligent mobile system<br/>4.2.2 Fundamentals of quantum mechanics<br/>4.3 Architecture of QIMS based on multi-quantum-agent<br/>4.3.1 Intelligent agent in quantum system<br/>4.3.2 Multi-quantum-agent system<br/>4.3.3 Architecture of QIMS based on MQAS<br/>4.4 Hardware of typical QIMS<br/>4.4.1 Quantum computers<br/>4.4.2 Classical-quantum hybrid components<br/>4.5 Planning for QIMS based on Grover algorithm<br/>4.5.1 A general framework of planning tasks .<br/>4.5.2 Grover algorithm<br/>4.5.3 Planning using Grover algorithm<br/>4.6 Learning for QIMS using QRL<br/>4.6.1 QRL<br/>4.6.2 Learning in unknown environment using QRL<br/>4.7 Discussion or ^he applications of QIMS<br/>4.8 Conclusions<br/>5 Quantum Associative Pattern Retrieval<br/>Carlo A. Trugenberger, Cristina M. Diamantini.<br/>5.1 Introduction<br/>5.2 Quantizing Neural Networks<br/>5.3 Quantum Associative Memories<br/>5.4 Phase Structure<br/>5.5 Summary<br/>6 Quantum-Inspired Evolutionary Algorithm for Numerical<br/>Optimization<br/>Andre Vargas Aba da Cruz, Marley M. B. R. Vellasco, Marco Aurelio<br/>C. Pacheco<br/>6.1 Introduction<br/>6.2 The Quantum-Inspired Evolutionary Algorithm using a Real<br/>Number Representation<br/>6.2.1 The Quantum Population<br/>6.2.2 Quantum Individuals Observation<br/>6.2.3 Updating the Quantum Population<br/>6.3 Case Studies<br/>6.3.1 Optimization of Benchmark Functions<br/>6.3.2 Discussion<br/>6.4 Conclusions and Future Works<br/>7 Calibration of the VGSSD Option Pricing Model<br/>using a Quantum-inspired Evolutionary Algorithm<br/>Kai Fan, Conall O'Sullivan, Anthony Brabazon, Michael O'Neill, Sean<br/>McCarraghy<br/>7.1 Introduction<br/>7.2 The Quantum-inspired Genetic Algorithm<br/>7.2.1 Representing a Quantum System<br/>7.2.2 Real-valued quantum-inspired evolutionary algorithms.<br/>7.3 Option Pricing Model Calibration<br/>7.4 Experimental Approach<br/>7.5 Results<br/>7.6 Conclusions1 Gaussian Quantum-Behaved Particle Swarm Optimization<br/>Applied to Fuzzy FID Controller Design<br/>Leandro dos Santos Coelho, Nadia Nedjah, Luiza de Macedo Mourelle<br/>1.1 Introduction<br/>1.2 Fuzzy Control Algorithm .<br/>1.2.1 Fuzzy PID controller<br/>1.3 Quantum-behaved Particle Swarm Optimization<br/>1.3.1 QPSO using Gaussian distribution<br/>1.4 Case Study and Simulation Results<br/>1.4.1 Description of Control Valve<br/>1.4.2 Simulation results<br/>1.5 Conclusion and Future Work<br/>2 Quantum-inspired genetic algorithms for flow shop<br/>scheduling<br/>Ling Wang^ Bin-bin Li.<br/>2.1 Introduction<br/>2.2 Permutation flow shop scheduling<br/>2.2.1 Problem statement<br/>2.2.2 GA-based multi-objective PFSS .<br/>2.3 Quantum-inspired genetic algorithm<br/>2.4 Hybrid QGA for single-objective PFSS<br/>2.4.1 QGA for single-objective PFSS<br/>2.4.2 Permutation-based GA<br/>2.4.3 Hybrid QGA for PFSS<br/>2.5 Hybrid QGA for multi-objective FSSP<br/>2.5.1 Solution Evaluation in Multi-objective Sense<br/>2.5.2 Population Trimming in QGA and PGA<br/>2.5.3 Genetic Operators and Stopping Criteria in QGA and PGA .<br/>2.5.4 Population Upadting in QGA and PGA<br/>VI Contents<br/>2.5.5 Procedure of QGA and PGA<br/>2.5.6 Hybrid Quantum-Inspired Genetic Algorithm<br/>2.6 Performance metrics of multi-objective optimization<br/>2.6.1 Overall Non-dominated Vector Generation {ONVG)<br/>2.6.2 C Metric (CM)<br/>2.6.3 Distance iMetrics {Dav and Dmax)<br/>2.6.4 Tan's Spacing (TS)<br/>2.6.5 Maximum Spread {MS)<br/>2.6.6 Average Quality {AQ)<br/>2.7 Simulation results and comparisons on single-objective PFFS<br/>2.8 Simulation results and comparisons on multi-objective PFFS<br/>2.8.1 Testing on existing problems in the literature<br/>2.8.2 Testing and comparisons on randomly generated instances<br/>2.9 Conclusions and future work<br/>]<br/>3 Quantum Simulataneous Recurrent Networks for Content<br/>Addressable Memory<br/>Raheel Allauddin, Stuart Boehmer, Elizabeth C. Behrman, Kavitha<br/>Gaddam, James E. Steak<br/>3.1 Introduction<br/>3.2 The Hopfield Model<br/>3.3 THE QUANTUM SYSTEM<br/>3.4 Mapping the Quantum System onto the Hopheld iNet<br/>3.5 QHN As An Information Propagator for a Microtubulin Architecture<br/>3.6 QHN As Simultaneous Recurrent Network<br/>3.7 Application: The CNOT Gate<br/>4 Quantum Intelligent Mobile System<br/>Chunlin Chen, Daoyi Dong<br/>4.1 Introduction<br/>4.2 Prerequisite<br/>4.2.1 Intelligent mobile system<br/>4.2.2 Fundamentals of quantum mechanics<br/>4.3 Architecture of QIMS based on multi-quantum-agent<br/>4.3.1 Intelligent agent in quantum system<br/>4.3.2 Multi-quantum-agent system<br/>4.3.3 Architecture of QIMS based on MQAS<br/>4.4 Hardware of typical QIMS<br/>4.4.1 Quantum computers<br/>4.4.2 Classical-quantum hybrid components<br/>4.5 Planning for QIMS based on Grover algorithm<br/>4.5.1 A general framework of planning tasks .<br/>4.5.2 Grover algorithm<br/>4.5.3 Planning using Grover algorithm<br/>4.6 Learning for QIMS using QRL<br/>4.6.1 QRL<br/>4.6.2 Learning in unknown environment using QRL<br/>4.7 Discussion or ^he applications of QIMS<br/>4.8 Conclusions<br/>5 Quantum Associative Pattern Retrieval<br/>Carlo A. Trugenberger, Cristina M. Diamantini.<br/>5.1 Introduction<br/>5.2 Quantizing Neural Networks<br/>5.3 Quantum Associative Memories<br/>5.4 Phase Structure<br/>5.5 Summary<br/>6 Quantum-Inspired Evolutionary Algorithm for Numerical<br/>Optimization<br/>Andre Vargas Aba da Cruz, Marley M. B. R. Vellasco, Marco Aurelio<br/>C. Pacheco<br/>6.1 Introduction<br/>6.2 The Quantum-Inspired Evolutionary Algorithm using a Real<br/>Number Representation<br/>6.2.1 The Quantum Population<br/>6.2.2 Quantum Individuals Observation<br/>6.2.3 Updating the Quantum Population<br/>6.3 Case Studies<br/>6.3.1 Optimization of Benchmark Functions<br/>6.3.2 Discussion<br/>6.4 Conclusions and Future Works<br/>7 Calibration of the VGSSD Option Pricing Model<br/>using a Quantum-inspired Evolutionary Algorithm<br/>Kai Fan, Conall O'Sullivan, Anthony Brabazon, Michael O'Neill, Sean<br/>McCarraghy<br/>7.1 Introduction<br/>7.2 The Quantum-inspired Genetic Algorithm<br/>7.2.1 Representing a Quantum System<br/>7.2.2 Real-valued quantum-inspired evolutionary algorithms.<br/>7.3 Option Pricing Model Calibration<br/>7.4 Experimental Approach<br/>7.5 Results<br/>7.6 Conclusions |
