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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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
Algorithm Portfolios : Advances, Applications, and Challenges / / by Dimitris Souravlias, Konstantinos E. Parsopoulos, Ilias S. Kotsireas, Panos M. Pardalos
Algorithm Portfolios : 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
Opac: Controlla la disponibilità qui
Applied Analysis, Optimization and Soft Computing : ICNAAO-2021, Varanasi, India, December 21–23 / / edited by Tanmoy Som, Debdas Ghosh, Oscar Castillo, Adrian Petrusel, Dayaram Sahu
Applied Analysis, Optimization and Soft Computing : ICNAAO-2021, Varanasi, India, December 21–23 / / edited by Tanmoy Som, Debdas Ghosh, Oscar Castillo, Adrian Petrusel, Dayaram Sahu
Autore Som Tanmoy
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (425 pages)
Disciplina 515
Altri autori (Persone) GhoshDebdas
CastilloOscar
PetruselAdrian
SahuDayaram
Collana Springer Proceedings in Mathematics & Statistics
Soggetto topico Mathematical optimization
Mathematical analysis
Differential equations
Mathematical models
Computer science
Optimization
Analysis
Differential Equations
Mathematical Modeling and Industrial Mathematics
Theory of Computation
Anàlisi matemàtica
Optimització matemàtica
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 981-9905-97-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Applied Analysis: M. Moga and A. Petruse, Large contractions and surjectivity in Banach spaces -- V. Tiwari, Binayak S., Tanmoy Som, On Hick’s contraction using a control function -- S. Binayak, N. Metiya, S. Kundu, P. Maity, Coupled fixed points for multivalued FengLiu type contractions with application to nonlinear integral equation -- R. Kumar, A. Kumar Nain, M. Kumar, On Unique Positive Solution of Hadamard Fractional Differential Equation Involving pLaplacian -- M. Pandey, T. Som, S. Verma, Dimensional Analysis of Mixed RiemannLiouville Fractional Integral of Vector Valued Functions -- T.M.C. Priyanka, A. Gowrisankar, Fractional operator associated with the fractal integral of A-fractal function -- Differential and Integral Equations: S. Chentout, A. Benmezai and W. Esserhan, Eigenvalue criteria for existence and nonexistence of positive solutions for αorder fractional differential equations, (2 < α ≤ 3), with integral condition ,on the halfline -- B. Hussain and A. Afroz, A Collocation Method for Solving Proportional Delay Riccati Differential Equations of Fractional Order -- Rahul and N. Kumar Mahato, On the Solution of Generalized Proportional Hadamard Fractional Integral Equations -- S. Ghosh and M. Banerjee, A multi-strain model for COVID-19 -- S. Samaddar, M. Dhar and P. Bhattacharya, Effect of Nonlinear Prey Refuge on Predator-Prey Dynamics -- Dr R. Tiwari, Effects of magnetic field and thermal conductivity variance on thermal excitation developed by laser pulses and thermal shock -- C. Bhuma, Parkinson Disease Detection from spirals and wave drawings using Sequential Model Selection -- Fractal Theory: P. Massopust, Clifford-Valued Fractal Interpolation -- M. Verma, A. Priyadarshi, S. Verma, Fractal dimension for a class of complexvalued fractal interpolation functions -- M. K Roychowdhury, Optimal quantizers for nonuniform distributions on Sierpi\'nski carpets -- V. Agrawal and T. Som, A note on complex-valued fractal functions on the Sierpinski Gasket -- Fuzzy Set Theory and Soft Computing: Salsabeela V and Sunil Jacob John, A Similarity Measure of Picture Fuzzy Soft Sets and its Application -- A. Kumari Prasad, Soft Almost s-Regularity and Soft Almost sNormality -- Fathima Perveen P A and Sunil Jacob John, Algebraic Properties of Spherical Fuzzy Sets -- T M Athira and Sunil Jacob John, Divergence Measures of Pythagorean Fuzzy Soft Sets -- Anitha K, Fuzzy-Rough Optimization Technique for Breast Cancer Classification -- Optimization: J. Dutta, An Invitation to Optimality Conditions through Non-smooth Analysis -- J. Jauny, D. Ghosh and A. Upadhayay, Multi-objective Environmentallly Friendly and Economically Feasible Electric Power Distribution Problem with Primal-Dual Interior-Point Method -- A. Singh and S. Prasad Yadav, Performance evaluation of DMUs using hybrid fuzzy multi-objective data envelopment analysis -- V. Laha and L. Pandey, On mathematical programs with equilibrium constraints under data uncertainty -- S. Ganguly, P. Das, Pricing policy with the effect of fairness concern, imprecise greenness and prices in imprecise market for a dual channel -- A. Sonkariya and S. P. Yadav, The best state based development of fuzzy DEA model -- D. Datta, Optimization Methods using Music Inspired Algorithm and its Comparison with Nature Inspired Algorithm -- A. Choudhary and S. Prasad Yadav, A new approach to solve intuitionistic fuzzy transportation problem -- M. Yadav and S. P. Yadav, Development of Intuitionistic fuzzy data envelopment analysis model based on interval data envelopment analysis model.
Record Nr. UNINA-9910731459903321
Som Tanmoy  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
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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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui