Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Active particles . Volume 3 : advances in theory, models, and applications / / Nicola Bellomo, José Antonio Carrillo, and Eitan Tadmor, editors
| Active particles . Volume 3 : advances in theory, models, and applications / / Nicola Bellomo, José Antonio Carrillo, and Eitan Tadmor, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (230 pages) |
| Disciplina | 519.3 |
| Collana | Modeling and Simulation in Science, Engineering and Technology |
| Soggetto topico |
Mathematical optimization
Mathematical optimization - Computer programs Models matemàtics Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-93302-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Variability and Heterogeneity in Natural Swarms: Experiments and Modeling -- 1 Introduction -- 2 Sources of Variability in Nature -- 2.1 Development as a Source of Variation -- 2.2 Transient Changes in the Behavior of Individuals -- 2.3 Environmentally Induced Variations -- 2.4 Social Structure -- 2.5 Inherent/Intrinsic Properties and Animal Personality -- 2.6 Variability in Microorganisms -- 3 Experiments with Heterogeneous Swarms -- 3.1 Fish -- 3.2 Mammals -- 3.3 Insects -- 3.4 Microorganisms -- 4 Modeling Heterogeneous Collective Motion -- 4.1 Continuous Models -- 4.2 Agent-Based Models -- 4.3 Specific Examples: Locust -- 4.4 Specific Examples: Microorganisms and Cells -- 5 Summary and Concluding Remarks -- References -- Active Crowds -- 1 Introduction -- 2 Models for Active Particles -- 2.1 Continuous Random Walks -- 2.1.1 Excluded-Volume Interactions -- 2.2 Discrete Random Walks -- 2.3 Hybrid Random Walks -- 3 Models for Externally Activated Particles -- 3.1 Continuous Models -- 3.2 Discrete Models -- 4 General Model Structure -- 4.1 Wasserstein Gradient Flows -- 4.2 Entropy Dissipation -- 5 Boundary Effects -- 5.1 Mass Conserving Boundary Conditions -- 5.2 Flux Boundary Conditions -- 5.3 Other Boundary Conditions -- 6 Active Crowds in the Life and Social Science -- 6.1 Pedestrian Dynamics -- 6.2 Transport in Biological Systems -- 7 Numerical Simulations -- 7.1 One Spatial Dimension -- 7.2 Two Spatial Dimensions -- References -- Mathematical Modeling of Cell Collective Motion Triggered by Self-Generated Gradients -- 1 Introduction -- 2 The Keller-Segel Model and Variations -- 2.1 The Construction of Waves by Keller and Segel -- 2.2 Positivity and Stability Issues -- 2.3 Variations on the Keller-Segel Model -- 2.4 Beyond the Keller-Segel Model: Two Scenarios for SGG.
3 Scenario 1: Strongest Advection at the Back -- 4 Scenario 2: Cell Leakage Compensated by Growth -- 5 Conclusion and Perspectives -- References -- Clustering Dynamics on Graphs: From Spectral Clustering to Mean Shift Through Fokker-Planck Interpolation -- 1 Introduction -- 1.1 Mean Shift-Based Methods -- 1.1.1 Lifting the Dynamics to the Wasserstein Space -- 1.2 Spectral Methods -- 1.2.1 Normalized Versions of the Graph Laplacian -- 1.2.2 More General Spectral Embeddings -- 1.3 Outline -- 2 Mean Shift and Fokker-Planck Dynamics on Graphs -- 2.1 Dynamic Interpretation of Spectral Embeddings -- 2.2 The Mean Shift Algorithm on Graphs -- 2.2.1 Mean Shift on Graphs as Inspired by Wasserstein Gradient Flows -- 2.2.2 Quickshift and KNF -- 3 Fokker-Planck Equations on Graphs -- 3.1 Fokker-Planck Equations on Graphs via Interpolation -- 3.2 Fokker-Planck Equation on Graphs via Reweighing and Connections to Graph Mean Shift -- 4 Continuum Limits of Fokker-Planck Equations on Graphs and Implications -- 4.1 Continuum Limit of Mean Shift Dynamics on Graphs -- 4.2 Continuum Limits of Fokker-Planck Equations on Graphs -- 4.3 The Witten Laplacian and Some Implications for Data Clustering -- 5 Numerical Examples -- 5.1 Numerical Method -- 5.2 Simulations -- 5.2.1 Graph Dynamics as Density Dynamics -- 5.2.2 Comparison of Graph Dynamics and PDE Dynamics -- 5.2.3 Clustering Dynamics -- 5.2.4 Effect of the Kernel Density Estimate on Clustering -- 5.2.5 Effect of Data Distribution on Clustering -- 5.2.6 Blue Sky Problem -- 5.2.7 Density vs. Geometry -- References -- Random Batch Methods for Classical and Quantum Interacting Particle Systems and Statistical Samplings -- 1 Introduction -- 2 The Random Batch Methods -- 2.1 The RBM Algorithms -- 2.2 Convergence Analysis -- 2.3 An Illustrating Example: Wealth Evolution -- 3 The Mean-Field Limit -- 4 Molecular Dynamics. 4.1 RBM with Kernel Splitting -- 4.2 Random Batch Ewald: An Importance Sampling in the Fourier Space -- 5 Statistical Sampling -- 5.1 Random Batch Monte Carlo for Many-Body Systems -- 5.2 RBM-SVGD: A Stochastic Version of Stein Variational Gradient Descent -- 6 Agent-Based Models for Collective Dynamics -- 6.1 The Cucker-Smale Model -- 6.2 Consensus Models -- 7 Quantum Dynamics -- 7.1 A Theoretical Result on the N-Body Schrödinger Equation -- 7.1.1 Mathematical Setting and Main Result -- 7.2 Quantum Monte Carlo Methods -- 7.2.1 The Random Batch Method for VMC -- 7.2.2 The Random Batch Method for DMC -- References -- Trends in Consensus-Based Optimization -- 1 Introduction -- 1.1 Notation and Assumptions -- 1.1.1 The Weighted Average -- 2 Consensus-Based Global Optimization Methods -- 2.1 Original Statement of the Method -- 2.1.1 Particle Scheme -- 2.1.2 Mean-Field Limit -- 2.1.3 Analytical Results for the Original Scheme Without Heaviside Function -- 2.1.4 Numerical Methods -- 2.2 Variant 1: Component-Wise Diffusion and Random Batches -- 2.2.1 Component-Wise Geometric Brownian Motion -- 2.2.2 Random Batch Method -- 2.2.3 Implementation and Numerical Results -- 2.3 Variant 2: Component-Wise Common Diffusion -- 2.3.1 Analytical Results -- 2.3.2 Numerical Results -- 3 Relationship of CBO and Particle Swarm Optimization -- 3.1 Variant 4: Personal Best Information -- 3.1.1 Performance -- 4 CBO with State Constraints -- 4.1 Variant 5: Dynamics Constrained to Hyper-Surfaces -- 4.1.1 Analytical Results -- 5 Overview of Applications -- 5.1 Global Optimization Problems: Comparison to Heuristic Methods -- 5.2 Machine Learning -- 5.3 Global Optimization with Constrained State Space -- 5.4 PDE Versus SDE Simulations -- 6 Conclusion, Outlook and Open problems -- References. |
| Record Nr. | UNISA-996466417303316 |
| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Active particles . Volume 3 : advances in theory, models, and applications / / Nicola Bellomo, José Antonio Carrillo, and Eitan Tadmor, editors
| Active particles . Volume 3 : advances in theory, models, and applications / / Nicola Bellomo, José Antonio Carrillo, and Eitan Tadmor, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (230 pages) |
| Disciplina | 519.3 |
| Collana | Modeling and Simulation in Science, Engineering and Technology |
| Soggetto topico |
Mathematical optimization
Mathematical optimization - Computer programs Models matemàtics Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-93302-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Variability and Heterogeneity in Natural Swarms: Experiments and Modeling -- 1 Introduction -- 2 Sources of Variability in Nature -- 2.1 Development as a Source of Variation -- 2.2 Transient Changes in the Behavior of Individuals -- 2.3 Environmentally Induced Variations -- 2.4 Social Structure -- 2.5 Inherent/Intrinsic Properties and Animal Personality -- 2.6 Variability in Microorganisms -- 3 Experiments with Heterogeneous Swarms -- 3.1 Fish -- 3.2 Mammals -- 3.3 Insects -- 3.4 Microorganisms -- 4 Modeling Heterogeneous Collective Motion -- 4.1 Continuous Models -- 4.2 Agent-Based Models -- 4.3 Specific Examples: Locust -- 4.4 Specific Examples: Microorganisms and Cells -- 5 Summary and Concluding Remarks -- References -- Active Crowds -- 1 Introduction -- 2 Models for Active Particles -- 2.1 Continuous Random Walks -- 2.1.1 Excluded-Volume Interactions -- 2.2 Discrete Random Walks -- 2.3 Hybrid Random Walks -- 3 Models for Externally Activated Particles -- 3.1 Continuous Models -- 3.2 Discrete Models -- 4 General Model Structure -- 4.1 Wasserstein Gradient Flows -- 4.2 Entropy Dissipation -- 5 Boundary Effects -- 5.1 Mass Conserving Boundary Conditions -- 5.2 Flux Boundary Conditions -- 5.3 Other Boundary Conditions -- 6 Active Crowds in the Life and Social Science -- 6.1 Pedestrian Dynamics -- 6.2 Transport in Biological Systems -- 7 Numerical Simulations -- 7.1 One Spatial Dimension -- 7.2 Two Spatial Dimensions -- References -- Mathematical Modeling of Cell Collective Motion Triggered by Self-Generated Gradients -- 1 Introduction -- 2 The Keller-Segel Model and Variations -- 2.1 The Construction of Waves by Keller and Segel -- 2.2 Positivity and Stability Issues -- 2.3 Variations on the Keller-Segel Model -- 2.4 Beyond the Keller-Segel Model: Two Scenarios for SGG.
3 Scenario 1: Strongest Advection at the Back -- 4 Scenario 2: Cell Leakage Compensated by Growth -- 5 Conclusion and Perspectives -- References -- Clustering Dynamics on Graphs: From Spectral Clustering to Mean Shift Through Fokker-Planck Interpolation -- 1 Introduction -- 1.1 Mean Shift-Based Methods -- 1.1.1 Lifting the Dynamics to the Wasserstein Space -- 1.2 Spectral Methods -- 1.2.1 Normalized Versions of the Graph Laplacian -- 1.2.2 More General Spectral Embeddings -- 1.3 Outline -- 2 Mean Shift and Fokker-Planck Dynamics on Graphs -- 2.1 Dynamic Interpretation of Spectral Embeddings -- 2.2 The Mean Shift Algorithm on Graphs -- 2.2.1 Mean Shift on Graphs as Inspired by Wasserstein Gradient Flows -- 2.2.2 Quickshift and KNF -- 3 Fokker-Planck Equations on Graphs -- 3.1 Fokker-Planck Equations on Graphs via Interpolation -- 3.2 Fokker-Planck Equation on Graphs via Reweighing and Connections to Graph Mean Shift -- 4 Continuum Limits of Fokker-Planck Equations on Graphs and Implications -- 4.1 Continuum Limit of Mean Shift Dynamics on Graphs -- 4.2 Continuum Limits of Fokker-Planck Equations on Graphs -- 4.3 The Witten Laplacian and Some Implications for Data Clustering -- 5 Numerical Examples -- 5.1 Numerical Method -- 5.2 Simulations -- 5.2.1 Graph Dynamics as Density Dynamics -- 5.2.2 Comparison of Graph Dynamics and PDE Dynamics -- 5.2.3 Clustering Dynamics -- 5.2.4 Effect of the Kernel Density Estimate on Clustering -- 5.2.5 Effect of Data Distribution on Clustering -- 5.2.6 Blue Sky Problem -- 5.2.7 Density vs. Geometry -- References -- Random Batch Methods for Classical and Quantum Interacting Particle Systems and Statistical Samplings -- 1 Introduction -- 2 The Random Batch Methods -- 2.1 The RBM Algorithms -- 2.2 Convergence Analysis -- 2.3 An Illustrating Example: Wealth Evolution -- 3 The Mean-Field Limit -- 4 Molecular Dynamics. 4.1 RBM with Kernel Splitting -- 4.2 Random Batch Ewald: An Importance Sampling in the Fourier Space -- 5 Statistical Sampling -- 5.1 Random Batch Monte Carlo for Many-Body Systems -- 5.2 RBM-SVGD: A Stochastic Version of Stein Variational Gradient Descent -- 6 Agent-Based Models for Collective Dynamics -- 6.1 The Cucker-Smale Model -- 6.2 Consensus Models -- 7 Quantum Dynamics -- 7.1 A Theoretical Result on the N-Body Schrödinger Equation -- 7.1.1 Mathematical Setting and Main Result -- 7.2 Quantum Monte Carlo Methods -- 7.2.1 The Random Batch Method for VMC -- 7.2.2 The Random Batch Method for DMC -- References -- Trends in Consensus-Based Optimization -- 1 Introduction -- 1.1 Notation and Assumptions -- 1.1.1 The Weighted Average -- 2 Consensus-Based Global Optimization Methods -- 2.1 Original Statement of the Method -- 2.1.1 Particle Scheme -- 2.1.2 Mean-Field Limit -- 2.1.3 Analytical Results for the Original Scheme Without Heaviside Function -- 2.1.4 Numerical Methods -- 2.2 Variant 1: Component-Wise Diffusion and Random Batches -- 2.2.1 Component-Wise Geometric Brownian Motion -- 2.2.2 Random Batch Method -- 2.2.3 Implementation and Numerical Results -- 2.3 Variant 2: Component-Wise Common Diffusion -- 2.3.1 Analytical Results -- 2.3.2 Numerical Results -- 3 Relationship of CBO and Particle Swarm Optimization -- 3.1 Variant 4: Personal Best Information -- 3.1.1 Performance -- 4 CBO with State Constraints -- 4.1 Variant 5: Dynamics Constrained to Hyper-Surfaces -- 4.1.1 Analytical Results -- 5 Overview of Applications -- 5.1 Global Optimization Problems: Comparison to Heuristic Methods -- 5.2 Machine Learning -- 5.3 Global Optimization with Constrained State Space -- 5.4 PDE Versus SDE Simulations -- 6 Conclusion, Outlook and Open problems -- References. |
| Record Nr. | UNINA-9910556880003321 |
| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in learning automata and intelligent optimization / / Javidan Kazemi Kordestani [and three others], editors
| Advances in learning automata and intelligent optimization / / Javidan Kazemi Kordestani [and three others], editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (355 pages) |
| Disciplina | 006.31 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Aprenentatge automàtic
Optimització matemàtica Mathematical optimization |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-76291-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Authors -- Abbreviations -- 1 An Introduction to Learning Automata and Optimization -- 1.1 Introduction -- 1.2 Learning Automata -- 1.2.1 Learning Automata Variants -- 1.2.2 Recent Applications of Learning Automata -- 1.3 Optimization -- 1.3.1 Evolutionary Algorithms and Swarm Intelligence -- 1.4 Reinforcement Learning and Optimization Methods -- 1.4.1 Static Optimization -- 1.4.2 Dynamic Optimization -- 1.5 LA and Optimization Timeline -- 1.6 Chapter Map -- 1.7 Conclusion -- References -- 2 Learning Automaton and Its Variants for Optimization: A Bibliometric Analysis -- 2.1 Introduction -- 2.2 Learning Automata Models and Optimization -- 2.3 Material and Method -- 2.3.1 Data Collection and Initial Results -- 2.3.2 Refining the Initial Results -- 2.4 Analyzing the Results -- 2.4.1 Initial Result Statistics -- 2.4.2 Top Journals -- 2.4.3 Top Researchers -- 2.4.4 Top Papers -- 2.4.5 Top Affiliations -- 2.4.6 Top Keywords -- 2.5 Conclusion -- References -- 3 Cellular Automata, Learning Automata, and Cellular Learning Automata for Optimization -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Cellular Automata -- 3.2.2 Learning Automata -- 3.2.3 Cellular Learning Automata -- 3.3 CA, CLA, and LA Models for Optimization -- 3.3.1 Cellular Learning Automata-Based Evolutionary Computing (CLA-EC) -- 3.3.2 Cooperative Cellular Learning Automata-Based Evolutionary Computing (CLA-EC) -- 3.3.3 Recombinative Cellular Learning Automata-Based Evolutionary Computing (RCLA-EC) -- 3.3.4 CLA-EC with Extremal Optimization (CLA-EC-EO) -- 3.3.5 Cellular Learning Automata-Based Differential Evolution (CLA-DE) -- 3.3.6 Cellular Particle Swarm Optimization (Cellular PSO) -- 3.3.7 Firefly Algorithm Based on Cellular Learning Automata (CLA-FA) -- 3.3.8 Harmony Search Algorithm Based on Learning Automata (LAHS).
3.3.9 Learning Automata Based Butterfly Optimization Algorithm (LABOA) -- 3.3.10 Grey Wolf Optimizer Based on Learning Automata (GWO-LA) -- 3.3.11 Learning Automata Models with Multiple Reinforcements (MLA) -- 3.3.12 Cellular Learning Automata Models with Multiple Reinforcements (MCLA) -- 3.3.13 Multi-reinforcement CLA with the Maximum Expected Rewards (MCLA) -- 3.3.14 Gravitational Search Algorithm Based on Learning Automata (GSA-LA) -- 3.4 Conclusion -- References -- 4 Learning Automata for Behavior Control in Evolutionary Computation -- 4.1 Introduction -- 4.2 Types of Parameter Adjustment in EC Community -- 4.2.1 EC with Constant Parameters -- 4.2.2 EC with Time-Varying Parameters -- 4.3 Differential Evolution -- 4.3.1 Initialization -- 4.3.2 Difference-Vector Based Mutation -- 4.3.3 Repair Operator -- 4.3.4 Crossover -- 4.3.5 Selection -- 4.4 Learning Automata for Adaptive Control of Behavior in Differential Evolution -- 4.4.1 Behavior Control in DE with Variable-Structure Learning Automaton -- 4.4.2 Behavior Control in DE with Fixed-Structure Learning Automaton -- 4.5 Experimental Setup -- 4.5.1 Benchmark Functions -- 4.5.2 Algorithm's Configuration -- 4.5.3 Simulation Settings and Results -- 4.5.4 Experimental Results -- 4.6 Conclusion -- References -- 5 A Memetic Model Based on Fixed Structure Learning Automata for Solving NP-Hard Problems -- 5.1 Introduction -- 5.2 Fixed Structure Learning Automata and Object Migrating Automata -- 5.2.1 Fixed Structure Learning Automata -- 5.2.2 Object Migration Automata -- 5.3 GALA -- 5.3.1 Global Search in GALA -- 5.3.2 Crossover Operator -- 5.3.3 Mutation Operator -- 5.3.4 Local Learning in GALA -- 5.3.5 Applications of GALA -- 5.4 The New Memetic Model Based on Fixed Structure Learning Automata -- 5.4.1 Hybrid Fitness Function -- 5.4.2 Mutation Operators -- 5.4.3 Crossover Operators. 5.5 The OneMax Problem -- 5.5.1 Local Search for OneMax -- 5.5.2 Experimental Results -- 5.6 Conclusion -- References -- 6 The Applications of Object Migration Automaton (OMA)-Memetic Algorithm for Solving NP-Hard Problems -- 6.1 Introduction -- 6.2 The Equipartitioning Problem -- 6.2.1 Local Search for EPP -- 6.2.2 Experimental Results -- 6.3 The Graph Isomorphism Problem -- 6.3.1 The Local Search in the Graph Isomorphism Problem -- 6.3.2 Experimental Results -- 6.4 Assignment of Cells to Switches Problem (ACTSP) in Cellular Mobile Network -- 6.4.1 Background and Related Work -- 6.4.2 The OMA-MA for Assignment of Cells to Switches Problem -- 6.4.3 The Framework of the OMA-MA Algorithm -- 6.4.4 Experimental Result -- 6.5 Conclusion -- References -- 7 An Overview of Multi-population Methods for Dynamic Environments -- 7.1 Introduction -- 7.2 Moving Peaks Benchmark -- 7.2.1 Extended Versions of MPB -- 7.3 Performance Measurement -- 7.4 Types of Multi-population Methods -- 7.4.1 Methods with a Fixed Number of Populations -- 7.4.2 Methods with a Variable Number of Populations -- 7.4.3 Methods Based on Population Clustering -- 7.4.4 Self-adapting the Number of Populations -- 7.5 Numerical Results -- 7.6 Conclusions -- References -- 8 Learning Automata for Online Function Evaluation Management in Evolutionary Multi-population Methods for Dynamic Optimization Problems -- 8.1 Introduction -- 8.2 Preliminaries -- 8.2.1 Waste of FEs Due to Change Detection -- 8.2.2 Waste of FEs Due to the Excessive Number of Sub-populations -- 8.2.3 Waste of FEs Due to Overcrowding of Subpopulations in the Same Area of the Search Space -- 8.2.4 Waste of FEs Due to Exclusion Operator -- 8.2.5 Allocation of FEs to Unproductive Populations -- 8.2.6 Unsuitable Parameter Configuration of the EC Methods -- 8.2.7 Equal Distribution of FEs Among Sub-populations. 8.3 Theory of Learning Automata -- 8.3.1 Fixed Structure Learning Automata -- 8.3.2 Variable Structure Learning Automata -- 8.4 EC Techniques under Study -- 8.4.1 Particle Swarm Optimization -- 8.4.2 Firefly Algorithm -- 8.4.3 Jaya -- 8.5 LA-Based FE Management Model for MP Evolutionary Dynamic Optimization -- 8.5.1 Initialization of Sub-populations -- 8.5.2 Detection and Response to Environmental Changes -- 8.5.3 Choose a Sub-population for Execution -- 8.5.4 Evaluate the Search Progress of Populations and Generate the Reinforcement Signal -- 8.5.5 Exclusion -- 8.6 FE-Management in MP Method with a Fixed Number of Populations -- 8.6.1 VSLA-Based FE Management Strategy -- 8.6.2 FSLA-Based FE Management Strategies -- 8.7 Experimental Study -- 8.7.1 Experimental Setup -- 8.7.2 Experimental Results and Discussion -- 8.8 Conclusion -- References -- 9 Function Management in Multi-population Methods with a Variable Number of Populations: A Variable Action Learning Automaton Approach -- 9.1 Introduction -- 9.2 Main Framework of Clustering Particle Swarm Optimization -- 9.2.1 Creating Multiple Sub-swarms from the Cradle Swarm -- 9.2.2 Local Search by PSO -- 9.2.3 Status of Sub-swarms -- 9.2.4 Detection and Response to Environmental Changes -- 9.3 Variable Action-Set Learning Automata -- 9.4 FEM in MP Methods with a Variable Number of Populations -- 9.5 Experimental Study -- 9.5.1 Dynamic Test Function -- 9.5.2 Performance Measure -- 9.5.3 Experimental Settings -- 9.5.4 Experimental Results -- 9.6 Conclusions -- References. |
| Record Nr. | UNINA-9910488695403321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algorithm Portfolios [[electronic resource] ] : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos
| Algorithm Portfolios [[electronic resource] ] : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos |
| Autore | Souravlias Dimitris |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (xiv, 92 pages) : illustrations |
| Disciplina | 518.1 |
| Collana | SpringerBriefs in Optimization |
| Soggetto topico |
Operations research
Management science Algorithms Microprogramming Discrete mathematics Operations Research, Management Science Control Structures and Microprogramming Discrete Mathematics Algorismes Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-68514-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Metaheuristic optimization algorithms -- 2. Algorithm portfolios -- 3. Selection of constituent algorithms -- 4. Allocation of computation resources -- 5. Sequential and parallel models -- 6. Recent applications -- 7. Epilogue -- References. |
| Record Nr. | UNINA-9910484845003321 |
Souravlias Dimitris
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algorithm Portfolios [[electronic resource] ] : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos
| Algorithm Portfolios [[electronic resource] ] : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos |
| Autore | Souravlias Dimitris |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (xiv, 92 pages) : illustrations |
| Disciplina | 518.1 |
| Collana | SpringerBriefs in Optimization |
| Soggetto topico |
Operations research
Management science Algorithms Microprogramming Discrete mathematics Operations Research, Management Science Control Structures and Microprogramming Discrete Mathematics Algorismes Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-68514-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Metaheuristic optimization algorithms -- 2. Algorithm portfolios -- 3. Selection of constituent algorithms -- 4. Allocation of computation resources -- 5. Sequential and parallel models -- 6. Recent applications -- 7. Epilogue -- References. |
| Record Nr. | UNISA-996466562303316 |
|
Souravlias Dimitris
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Applied and computational optimal control : a control parametrization approach / / Kok Lay Teo, Bin Li, Changjun Yu, Volker Rehbock
| Applied and computational optimal control : a control parametrization approach / / Kok Lay Teo, Bin Li, Changjun Yu, Volker Rehbock |
| Autore | Teo K. L. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (581 pages) |
| Disciplina | 519.6 |
| Collana | Springer Optimization and Its Applications |
| Soggetto topico |
Constrained optimization
Control theory - Mathematics Teoria de control Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-69913-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction -- 2 Unconstrained Optimization Techniques -- 3 Constrained Mathematical Programming -- 4 Optimization Problems Subject to Continuous Inequality Constraints -- 5 Discrete Time Optimal Control Problems -- 6 Elements of Optimal Control Theory -- 7 Gradient Formulae for Optimal Parameter Selection Problems -- 8 Control Parametrization for Canonical Optimal Control Problems -- 9 Optimal Control Problems with State and Control Constraints -- 10 Time-Lag Optimal Control Problems -- 11 Feedback Control -- 12 On Some Special Classes of Stochastic Optimal Control Problems -- A.1 Elements of Mathematical Analysis -- A.2 Global Optimization via Filled Function Approach -- A.3 Elements of Probability Theory |
| Record Nr. | UNISA-996466413103316 |
|
Teo K. L.
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Applied and computational optimal control : a control parametrization approach / / Kok Lay Teo, Bin Li, Changjun Yu, Volker Rehbock
| Applied and computational optimal control : a control parametrization approach / / Kok Lay Teo, Bin Li, Changjun Yu, Volker Rehbock |
| Autore | Teo K. L. |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (581 pages) |
| Disciplina | 519.6 |
| Collana | Springer Optimization and Its Applications |
| Soggetto topico |
Constrained optimization
Control theory - Mathematics Teoria de control Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-69913-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Introduction -- 2 Unconstrained Optimization Techniques -- 3 Constrained Mathematical Programming -- 4 Optimization Problems Subject to Continuous Inequality Constraints -- 5 Discrete Time Optimal Control Problems -- 6 Elements of Optimal Control Theory -- 7 Gradient Formulae for Optimal Parameter Selection Problems -- 8 Control Parametrization for Canonical Optimal Control Problems -- 9 Optimal Control Problems with State and Control Constraints -- 10 Time-Lag Optimal Control Problems -- 11 Feedback Control -- 12 On Some Special Classes of Stochastic Optimal Control Problems -- A.1 Elements of Mathematical Analysis -- A.2 Global Optimization via Filled Function Approach -- A.3 Elements of Probability Theory |
| Record Nr. | UNINA-9910482957303321 |
|
Teo K. L.
|
||
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied fractional calculus in identification and control / / Utkal Mehta, Kishore Bingi, Sahaj Saxena (editors)
| Applied fractional calculus in identification and control / / Utkal Mehta, Kishore Bingi, Sahaj Saxena (editors) |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (212 pages) |
| Disciplina | 515.83 |
| Collana | Studies in infrastructure and control |
| Soggetto topico |
Fractional calculus
Mathematical optimization Control automàtic Càlcul fraccional Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-19-3501-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editors -- 1 Fractional Calculus for Multivariate Vector-Valued Function and Fractal Function -- 1 Introduction -- 2 Katugampola Fractional Integral of a Multivariate Vector-Valued Function -- 3 Fractional Derivative of a Fractal Function -- 4 Conclusion -- References -- 2 Synchronization of Stochastic Fractional Chaotic Systems -- 1 Introduction -- 2 Preliminaries -- 3 Main Results -- 4 Examples -- 5 Conclusion -- References -- 3 Fractional-Order Comb Filter Design For Power-Line Interference Removal -- 1 Introduction -- 2 Background of Fractional-Order Filter Design -- 3 Proposed Approach for Fractional-Order Comb Filter -- 4 Design and Verification -- 5 Conclusions -- References -- 4 Practical Realization of Fractional-Order Notch Filter with Asymmetric Slopes and Optimized Quality Factor -- 1 Introduction -- 2 Fractional-Order Notch Filter -- 3 Optimizing Filter Coefficients for Real-Time Implementation -- 4 Simulation and Experimental Study -- 5 Conclusions -- References -- 5 Fractional Order Modified IMC for Integrating Processes for Given Stability Margins and Increased Closed Loop Bandwidth -- 1 Introduction -- 2 Controller Design -- 2.1 Process-1 -- 2.2 Process-2 -- 2.3 Evaluation of Controller Parameters -- 2.4 Solution Philosophy -- 2.5 Finding beta Subscript a Baseline element of normal upper Delta Subscript aβa inΔa -- 2.6 Finding beta Subscript b Baseline element of normal upper Delta Subscript bβb inΔb -- 3 Simulation Examples -- 3.1 Example-1 -- 3.2 Example-2 -- 4 Discussions and Conclusions -- References -- 6 Internal Model Control-Based Fractional Order Controller Design for Process Plants Satisfying Desired Gain Margin and Phase Margin -- 1 Introduction -- 1.1 Motivation to Not Approximating the Delay Term in IMC -- 2 Internal Model Controller.
2.1 upper A Subscript mAm and phi Subscript mφm Specifications -- 2.2 Design Philosophy -- 2.3 Finding omega Subscript p Baseline element of normal upper Xi Subscript aωpinΞa -- 2.4 Finding omega Subscript g Baseline element of normal upper Xi Subscript bωg inΞb -- 2.5 Disturbance Rejection Analysis -- 2.6 Algorithm for Determining FO-IMC Controller Parameters -- 3 Simulation Studies and Experimental Validation -- 3.1 Example-I -- 3.2 Example-II -- 3.3 Example-III -- 3.4 Experimental Validation -- 4 Discussion and Conclusion -- References -- 7 Novel Hybrid Iterative Learning-Fractional Predicative PI Controller for Time-Delay Systems -- 1 Introduction -- 2 Methodology -- 2.1 Controller Evolution -- 2.2 Iterative Learning Control -- 3 Results and Discussion -- 3.1 Process Model Selection -- 3.2 L- and Q-Filter Parameters Identification -- 3.3 FOPDT Process Model -- 3.4 SOPDT Process Model -- 3.5 Summary -- 4 Conclusion -- References -- 8 Design of Robust Model Predictive Controller for DC Motor Using Fractional Calculus -- 1 Introduction -- 2 Modeling of DC Motors with a Fractional Order -- 3 Design of Fractional Model Predictive Controller (FMPC) for DC Motor Model -- 4 Checking the Robustness of the Response of the Fractional-Order Model -- 5 Conclusion -- References -- 9 Studying Fractional-Order Controller Structures for Load Frequency Control of Interconnected Multiple Source Power System -- 1 Introduction -- 2 Concept Explanation -- 2.1 Chosen Application -- 3 Control Optimization Method -- 3.1 Bubble Net Attacking Method -- 3.2 Robust Tuning Performance Index -- 4 Analysis -- 4.1 Load Disturbance Approach-Scenario 1 -- 4.2 Load Disturbance Approach-Scenario 2 -- 4.3 Parameter Perturbation Approach -- 5 Conclusions -- References -- 10 Hardware Implementation of the Fractional Controller on Quadrotor Aircraft -- 1 Introduction. 2 Preliminary Concepts -- 3 Euler Angles Representation of 3D Rotation -- 4 Quadrotor Body Dynamics -- 5 Quadrotor Actuator Dynamics -- 6 Quadrotor State-Space Model -- 7 Quadrotor Linear Model -- 8 Quadrotor Transfer Functions for Control Design -- 9 Numeric Computation of Fractional-Order Blocks for Hardware Implementation -- 10 Hardware Station Setup -- 10.1 Hardware Experimentation Workflow Scheme -- 11 Analysing Experimentation results -- 12 Conclusions -- References -- 11 Optimum Fractional-Order PID for Active Suspension of Quarter Car Model Control -- 1 Introduction -- 2 Mathematical Model of Quarter Car Model System -- 3 Brief Design of FO-PID Controller -- 4 Simulation Parameters -- 5 Results and Discussions -- 6 Conclusion -- References. |
| Record Nr. | UNINA-9910592986703321 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied fractional calculus in identification and control / / Utkal Mehta, Kishore Bingi, Sahaj Saxena (editors)
| Applied fractional calculus in identification and control / / Utkal Mehta, Kishore Bingi, Sahaj Saxena (editors) |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (212 pages) |
| Disciplina | 515.83 |
| Collana | Studies in infrastructure and control |
| Soggetto topico |
Fractional calculus
Mathematical optimization Control automàtic Càlcul fraccional Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-19-3501-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editors -- 1 Fractional Calculus for Multivariate Vector-Valued Function and Fractal Function -- 1 Introduction -- 2 Katugampola Fractional Integral of a Multivariate Vector-Valued Function -- 3 Fractional Derivative of a Fractal Function -- 4 Conclusion -- References -- 2 Synchronization of Stochastic Fractional Chaotic Systems -- 1 Introduction -- 2 Preliminaries -- 3 Main Results -- 4 Examples -- 5 Conclusion -- References -- 3 Fractional-Order Comb Filter Design For Power-Line Interference Removal -- 1 Introduction -- 2 Background of Fractional-Order Filter Design -- 3 Proposed Approach for Fractional-Order Comb Filter -- 4 Design and Verification -- 5 Conclusions -- References -- 4 Practical Realization of Fractional-Order Notch Filter with Asymmetric Slopes and Optimized Quality Factor -- 1 Introduction -- 2 Fractional-Order Notch Filter -- 3 Optimizing Filter Coefficients for Real-Time Implementation -- 4 Simulation and Experimental Study -- 5 Conclusions -- References -- 5 Fractional Order Modified IMC for Integrating Processes for Given Stability Margins and Increased Closed Loop Bandwidth -- 1 Introduction -- 2 Controller Design -- 2.1 Process-1 -- 2.2 Process-2 -- 2.3 Evaluation of Controller Parameters -- 2.4 Solution Philosophy -- 2.5 Finding beta Subscript a Baseline element of normal upper Delta Subscript aβa inΔa -- 2.6 Finding beta Subscript b Baseline element of normal upper Delta Subscript bβb inΔb -- 3 Simulation Examples -- 3.1 Example-1 -- 3.2 Example-2 -- 4 Discussions and Conclusions -- References -- 6 Internal Model Control-Based Fractional Order Controller Design for Process Plants Satisfying Desired Gain Margin and Phase Margin -- 1 Introduction -- 1.1 Motivation to Not Approximating the Delay Term in IMC -- 2 Internal Model Controller.
2.1 upper A Subscript mAm and phi Subscript mφm Specifications -- 2.2 Design Philosophy -- 2.3 Finding omega Subscript p Baseline element of normal upper Xi Subscript aωpinΞa -- 2.4 Finding omega Subscript g Baseline element of normal upper Xi Subscript bωg inΞb -- 2.5 Disturbance Rejection Analysis -- 2.6 Algorithm for Determining FO-IMC Controller Parameters -- 3 Simulation Studies and Experimental Validation -- 3.1 Example-I -- 3.2 Example-II -- 3.3 Example-III -- 3.4 Experimental Validation -- 4 Discussion and Conclusion -- References -- 7 Novel Hybrid Iterative Learning-Fractional Predicative PI Controller for Time-Delay Systems -- 1 Introduction -- 2 Methodology -- 2.1 Controller Evolution -- 2.2 Iterative Learning Control -- 3 Results and Discussion -- 3.1 Process Model Selection -- 3.2 L- and Q-Filter Parameters Identification -- 3.3 FOPDT Process Model -- 3.4 SOPDT Process Model -- 3.5 Summary -- 4 Conclusion -- References -- 8 Design of Robust Model Predictive Controller for DC Motor Using Fractional Calculus -- 1 Introduction -- 2 Modeling of DC Motors with a Fractional Order -- 3 Design of Fractional Model Predictive Controller (FMPC) for DC Motor Model -- 4 Checking the Robustness of the Response of the Fractional-Order Model -- 5 Conclusion -- References -- 9 Studying Fractional-Order Controller Structures for Load Frequency Control of Interconnected Multiple Source Power System -- 1 Introduction -- 2 Concept Explanation -- 2.1 Chosen Application -- 3 Control Optimization Method -- 3.1 Bubble Net Attacking Method -- 3.2 Robust Tuning Performance Index -- 4 Analysis -- 4.1 Load Disturbance Approach-Scenario 1 -- 4.2 Load Disturbance Approach-Scenario 2 -- 4.3 Parameter Perturbation Approach -- 5 Conclusions -- References -- 10 Hardware Implementation of the Fractional Controller on Quadrotor Aircraft -- 1 Introduction. 2 Preliminary Concepts -- 3 Euler Angles Representation of 3D Rotation -- 4 Quadrotor Body Dynamics -- 5 Quadrotor Actuator Dynamics -- 6 Quadrotor State-Space Model -- 7 Quadrotor Linear Model -- 8 Quadrotor Transfer Functions for Control Design -- 9 Numeric Computation of Fractional-Order Blocks for Hardware Implementation -- 10 Hardware Station Setup -- 10.1 Hardware Experimentation Workflow Scheme -- 11 Analysing Experimentation results -- 12 Conclusions -- References -- 11 Optimum Fractional-Order PID for Active Suspension of Quarter Car Model Control -- 1 Introduction -- 2 Mathematical Model of Quarter Car Model System -- 3 Brief Design of FO-PID Controller -- 4 Simulation Parameters -- 5 Results and Discussions -- 6 Conclusion -- References. |
| Record Nr. | UNISA-996490346903316 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Applied optimization and swarm intelligence / / 236 pages
| Applied optimization and swarm intelligence / / 236 pages |
| Autore | Osaba Eneko |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (236 pages) |
| Disciplina | 006.3824 |
| Collana | Springer tracts in nature-inspired computing |
| Soggetto topico |
Swarm intelligence
Intel·ligència artificial Optimització matemàtica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-0662-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910767545703321 |
|
Osaba Eneko
|
||
| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
