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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 | ||
| ||
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 | ||
| ||
Advances in Optimization and Applications : 14th International Conference, OPTIMA 2023, Petrovac, Montenegro, September 18–22, 2023, Revised Selected Papers / / edited by Nicholas Olenev, Yuri Evtushenko, Milojica Jaćimović, Michael Khachay, Vlasta Malkova
| Advances in Optimization and Applications : 14th International Conference, OPTIMA 2023, Petrovac, Montenegro, September 18–22, 2023, Revised Selected Papers / / edited by Nicholas Olenev, Yuri Evtushenko, Milojica Jaćimović, Michael Khachay, Vlasta Malkova |
| Autore | Olenev Nicholas |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (296 pages) |
| Disciplina | 004.0151 |
| Altri autori (Persone) |
EvtushenkoYuri
JaćimovićMilojica KhachayMichael MalkovaVlasta |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Computer science - Mathematics
Artificial intelligence Computer science Mathematical Applications in Computer Science Mathematics of Computing Artificial Intelligence Theory of Computation Optimització matemàtica Informàtica Processament òptic de dades Xarxes d'ordinadors |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN |
9783031487514
3031487516 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910770242703321 |
Olenev Nicholas
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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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 |
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Souravlias Dimitris
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Analytics Modeling in Reliability and Machine Learning and Its Applications / / edited by Hoang Pham
| Analytics Modeling in Reliability and Machine Learning and Its Applications / / edited by Hoang Pham |
| Autore | Pham Hoang |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (480 pages) |
| Disciplina | 006.31 |
| Collana | Springer Series in Reliability Engineering |
| Soggetto topico |
Machine learning
Computers Medical care Industrial engineering Production engineering Mathematical optimization Aerospace engineering Astronautics Machine Learning Hardware Performance and Reliability Health Care Industrial and Production Engineering Optimization Aerospace Technology and Astronautics Aprenentatge automàtic Ordinadors Assistència sanitària Enginyeria industrial Optimització matemàtica Astronàutica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783031726361
9783031726354 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Preface -- 1. Reliability Analysis For Inventory Management For Repair Parts Based on Imperfect Data.-2. Improved Industrial Risk Analysis via a Human Factor-driven Bayesian Network Approach -- 3. Unsupervised Representation Learning Approach for Intrusion Detection in the Industrial Internet of Things Network Environment -- 4. Aero-engine Life Prediction Based on ARIMA and LSTM with Multi-Head Attention Mechanism -- 5. Human-Machine Integration to Strengthen Risk Management in the Winemaking Industry -- 6. One-Class Classification for Credit Card Fraud Detection: A Detailed Study with Comparative Insights from Binary Classification -- 7. Performance Analysis of Big Transfer Models on Biomedical Image Classification -- 8. Machine Learning Approach for Testing the Efficiency of Software Reliability Estimators of Weibull Class Models -- 9. Holistic Perishable Pharmaceutical Inventory Management System -- 10. Optimum Switch Self-Check Interval for Safety-Critical Device Mission Reliability -- 11. Accurate Estimation of Cargo Power Using Machine Learning Algorithms -- 12. Digital Transformation in Software Quality Assurance -- 13. Stress Studies: A Review -- 14. Higher Order Dynamic Mode Decomposition-based Timeseries Forecasting for Covid-19 -- 15. System Trustability: New Concept and Applications -- 16. Digital Twin Implementation in Small and Medium Size Enterprises: A Case Study -- 17. Software Reliability Modeling: A Review. |
| Record Nr. | UNINA-9910983346303321 |
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Pham Hoang
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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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 |
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Som Tanmoy
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| 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 |
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Teo K. L.
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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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. | UNINA-9910482957303321 |
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Teo K. L.
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Soggetto topico
-
Optimització matemàtica
(140)
- Mathematical optimization (87)
- Optimization (21)
- Calculus of variations (19)
- Teoria de control (17)