top

  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.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Particle swarm optimization / / Maurice Clerc
Particle swarm optimization / / Maurice Clerc
Autore Clerc Maurice
Pubbl/distr/stampa London ; ; Newport Beach : , : ISTE, , 2006
Descrizione fisica 1 online resource (245 pages)
Disciplina 006.3
519.62
Collana ISTE
Soggetto topico Mathematical optimization
Particles (Nuclear physics)
Swarm intelligence
Soggetto genere / forma Electronic books.
ISBN 9780470612163
9786610510566
1-84704-454-9
0-470-61216-9
0-470-39443-9
1-84704-554-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Particle Swarm Optimization; Table of Contents; Foreword; Introduction; Part I. Particle Swarm Optimization; Chapter 1. What is a Difficult Problem?; 1.1. An intrinsic definition.; 1.2. Estimation and practical measurement.; 1.3. For "amatheurs": some estimates of difficulty; 1.3.1. Function xd; 1.3.2. Function x2d; 1.3.3. Function xd|sin(xd)|; 1.3.4. Traveling salesman on D cities; 1.4. Summary; Chapter 2. On a Table Corner; 2.1. Apiarian metaphor; 2.2. An aside on the spreading of a rumor; 2.3. Abstract formulation; 2.4. What is really transmitted; 2.5. Cooperation versus competition; 2.6. For "amatheurs": a simple calculation of propagation of rumor2.7. Summary; Chapter 3. First Formulations; 3.1. Minimal version; 3.1.1. Swarm size; 3.1.2. Information links; 3.1.3. Initialization; 3.1.4. Equations of motion; 3.1.5. Interval confinement; 3.1.6. Proximity distributions; 3.2. Two common errors; 3.3. Principal drawbacks of this formulation; 3.3.1. Distribution bias; 3.3.2. Explosion and maximum velocity; 3.4. Manual parameter setting; 3.5. For "amatheurs": average number of informants; 3.6. Summary; Chapter 4. Benchmark Set; 4.1. What is the purpose of test functions?; 4.2. Six reference functions4.3. Representations and comments; 4.4. For "amatheurs": estimates of levels of difficulty; 4.4.1. Theoretical difficulty; 4.4.1.1. Tripod; 4.4.1.2. Alpine 10D; 4.4.1.3. Rosenbrock; 4.4.2. Difficulty according to the search effort; 4.5. Summary; Chapter 5. Mistrusting Chance; 5.1. Analysis of an anomaly; 5.2. Computing randomness; 5.3. Reproducibility; 5.4. On numerical precision; 5.5. The rare KISS; 5.5.1. Brief description; 5.5.2. Test of KISS; 5.6. On the comparison of results; 5.7. For "amatheurs": confidence in the estimate of a rate of failure; 5.8. C programs5.9. Summary; Chapter 6. First Results; 6.1. A simple program; 6.2. Overall results; 6.3. Robustness and performance maps; 6.4. Theoretical difficulty and noted difficulty; 6.5. Source code of OEP 0; 6.6. Summary; Chapter 7. Swarm: Memory and Graphs of Influence; 7.1. Circular neighborhood of the historical PSO; 7.2. Memory-swarm; 7.3. Fixed topologies; 7.4. Random variable topologies; 7.4.1. Direct recruitment; 7.4.2. Recruitment by common channel of communication; 7.5. Influence of the number of informants; 7.5.1. In fixed topology; 7.5.2. In random variable topology; 7.6. Influence of the number of memories7.7. Reorganizations of the memory-swarm; 7.7.1. Mixing of the memories; 7.7.2. Queen and other centroids; 7.7.3. Comparative results; 7.8. For "amatheurs": temporal connectivity in random recruitment; 7.9. Summary; Chapter 8. Distributions of Proximity; 8.1. The random possibilities; 8.2. Review of rectangular distribution; 8.3. Alternative distributions of possibilities; 8.3.1. Ellipsoidal positive sectors; 8.3.2. Independent Gaussians; 8.3.3. Local by independent Gaussians; 8.3.4. The class of one-dimensional distributions; 8.3.5. Pivots; 8.3.6. Adjusted ellipsoids
Record Nr. UNINA-9910143311203321
Clerc Maurice  
London ; ; Newport Beach : , : ISTE, , 2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Particle swarm optimization / / Maurice Clerc
Particle swarm optimization / / Maurice Clerc
Autore Clerc Maurice
Pubbl/distr/stampa London ; ; Newport Beach : , : ISTE, , 2006
Descrizione fisica 1 online resource (245 pages)
Disciplina 006.3
519.62
Collana ISTE
Soggetto topico Mathematical optimization
Particles (Nuclear physics)
Swarm intelligence
ISBN 9780470612163
9786610510566
1-84704-454-9
0-470-61216-9
0-470-39443-9
1-84704-554-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Particle Swarm Optimization; Table of Contents; Foreword; Introduction; Part I. Particle Swarm Optimization; Chapter 1. What is a Difficult Problem?; 1.1. An intrinsic definition.; 1.2. Estimation and practical measurement.; 1.3. For "amatheurs": some estimates of difficulty; 1.3.1. Function xd; 1.3.2. Function x2d; 1.3.3. Function xd|sin(xd)|; 1.3.4. Traveling salesman on D cities; 1.4. Summary; Chapter 2. On a Table Corner; 2.1. Apiarian metaphor; 2.2. An aside on the spreading of a rumor; 2.3. Abstract formulation; 2.4. What is really transmitted; 2.5. Cooperation versus competition; 2.6. For "amatheurs": a simple calculation of propagation of rumor2.7. Summary; Chapter 3. First Formulations; 3.1. Minimal version; 3.1.1. Swarm size; 3.1.2. Information links; 3.1.3. Initialization; 3.1.4. Equations of motion; 3.1.5. Interval confinement; 3.1.6. Proximity distributions; 3.2. Two common errors; 3.3. Principal drawbacks of this formulation; 3.3.1. Distribution bias; 3.3.2. Explosion and maximum velocity; 3.4. Manual parameter setting; 3.5. For "amatheurs": average number of informants; 3.6. Summary; Chapter 4. Benchmark Set; 4.1. What is the purpose of test functions?; 4.2. Six reference functions4.3. Representations and comments; 4.4. For "amatheurs": estimates of levels of difficulty; 4.4.1. Theoretical difficulty; 4.4.1.1. Tripod; 4.4.1.2. Alpine 10D; 4.4.1.3. Rosenbrock; 4.4.2. Difficulty according to the search effort; 4.5. Summary; Chapter 5. Mistrusting Chance; 5.1. Analysis of an anomaly; 5.2. Computing randomness; 5.3. Reproducibility; 5.4. On numerical precision; 5.5. The rare KISS; 5.5.1. Brief description; 5.5.2. Test of KISS; 5.6. On the comparison of results; 5.7. For "amatheurs": confidence in the estimate of a rate of failure; 5.8. C programs5.9. Summary; Chapter 6. First Results; 6.1. A simple program; 6.2. Overall results; 6.3. Robustness and performance maps; 6.4. Theoretical difficulty and noted difficulty; 6.5. Source code of OEP 0; 6.6. Summary; Chapter 7. Swarm: Memory and Graphs of Influence; 7.1. Circular neighborhood of the historical PSO; 7.2. Memory-swarm; 7.3. Fixed topologies; 7.4. Random variable topologies; 7.4.1. Direct recruitment; 7.4.2. Recruitment by common channel of communication; 7.5. Influence of the number of informants; 7.5.1. In fixed topology; 7.5.2. In random variable topology; 7.6. Influence of the number of memories7.7. Reorganizations of the memory-swarm; 7.7.1. Mixing of the memories; 7.7.2. Queen and other centroids; 7.7.3. Comparative results; 7.8. For "amatheurs": temporal connectivity in random recruitment; 7.9. Summary; Chapter 8. Distributions of Proximity; 8.1. The random possibilities; 8.2. Review of rectangular distribution; 8.3. Alternative distributions of possibilities; 8.3.1. Ellipsoidal positive sectors; 8.3.2. Independent Gaussians; 8.3.3. Local by independent Gaussians; 8.3.4. The class of one-dimensional distributions; 8.3.5. Pivots; 8.3.6. Adjusted ellipsoids
Record Nr. UNISA-996217137603316
Clerc Maurice  
London ; ; Newport Beach : , : ISTE, , 2006
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Practical genetic algorithms [[electronic resource] /] / Randy L. Haupt, Sue Ellen Haupt
Practical genetic algorithms [[electronic resource] /] / Randy L. Haupt, Sue Ellen Haupt
Autore Haupt Randy L
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, N.J., : John Wiley, c2004
Descrizione fisica 1 online resource (273 p.)
Disciplina 519.62
Altri autori (Persone) HauptS. E
Soggetto topico Genetic algorithms
ISBN 1-280-54212-8
9786610542123
0-471-67175-4
0-471-67174-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto PRACTICAL GENETIC ALGORITHMS; CONTENTS; Preface; Preface to First Edition; List of Symbols; 1 Introduction to Optimization; 1.1 Finding the Best Solution; 1.1.1 What Is Optimization?; 1.1.2 Root Finding versus Optimization; 1.1.3 Categories of Optimization; 1.2 Minimum-Seeking Algorithms; 1.2.1 Exhaustive Search; 1.2.2 Analytical Optimization; 1.2.3 Nelder-Mead Downhill Simplex Method; 1.2.4 Optimization Based on Line Minimization; 1.3 Natural Optimization Methods; 1.4 Biological Optimization: Natural Selection; 1.5 The Genetic Algorithm; Bibliography; Exercises
2 The Binary Genetic Algorithm2.1 Genetic Algorithms: Natural Selection on a Computer; 2.2 Components of a Binary Genetic Algorithm; 2.2.1 Selecting the Variables and the Cost Function; 2.2.2 Variable Encoding and Decoding; 2.2.3 The Population; 2.2.4 Natural Selection; 2.2.5 Selection; 2.2.6 Mating; 2.2.7 Mutations; 2.2.8 The Next Generation; 2.2.9 Convergence; 2.3 A Parting Look; Bibliography; Exercises; 3 The Continuous Genetic Algorithm; 3.1 Components of a Continuous Genetic Algorithm; 3.1.1 The Example Variables and Cost Function; 3.1.2 Variable Encoding, Precision, and Bounds
3.1.3 Initial Population3.1.4 Natural Selection; 3.1.5 Pairing; 3.1.6 Mating; 3.1.7 Mutations; 3.1.8 The Next Generation; 3.1.9 Convergence; 3.2 A Parting Look; Bibliography; Exercises; 4 Basic Applications; 4.1 "Mary Had a Little Lamb"; 4.2 Algorithmic Creativity-Genetic Art; 4.3 Word Guess; 4.4 Locating an Emergency Response Unit; 4.5 Antenna Array Design; 4.6 The Evolution of Horses; 4.5 Summary; Bibliography; 5 An Added Level of Sophistication; 5.1 Handling Expensive Cost Functions; 5.2 Multiple Objective Optimization; 5.2.1 Sum of Weighted Cost Functions; 5.2.2 Pareto Optimization
5.3 Hybrid GA5.4 Gray Codes; 5.5 Gene Size; 5.6 Convergence; 5.7 Alternative Crossovers for Binary GAs; 5.8 Population; 5.9 Mutation; 5.10 Permutation Problems; 5.11 Selecting GA Parameters; 5.12 Continuous versus Binary GA; 5.13 Messy Genetic Algorithms; 5.14 Parallel Genetic Algorithms; 5.14.1 Advantages of Parallel GAs; 5.14.2 Strategies for Parallel GAs; 5.14.3 Expected Speedup; 5.14.4 An Example Parallel GA; 5.14.5 How Parallel GAs Are Being Used; Bibliography; Exercises; 6 Advanced Applications; 6.1 Traveling Salesperson Problem; 6.2 Locating an Emergency Response Unit Revisited
6.3 Decoding a Secret Message6.4 Robot Trajectory Planning; 6.5 Stealth Design; 6.6 Building Dynamic Inverse Models-The Linear Case; 6.7 Building Dynamic Inverse Models-The Nonlinear Case; 6.8 Combining GAs with Simulations-Air Pollution Receptor Modeling; 6.9 Optimizing Artificial Neural Nets with GAs; 6.10 Solving High-Order Nonlinear Partial Differential Equations; Bibliography; 7 More Natural Optimization Algorithms; 7.1 Simulated Annealing; 7.2 Particle Swarm Optimization (PSO); 7.3 Ant Colony Optimization (ACO); 7.4 Genetic Programming (GP); 7.5 Cultural Algorithms
7.6 Evolutionary Strategies
Record Nr. UNINA-9910145765503321
Haupt Randy L  
Hoboken, N.J., : John Wiley, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Practical genetic algorithms [[electronic resource] /] / Randy L. Haupt, Sue Ellen Haupt
Practical genetic algorithms [[electronic resource] /] / Randy L. Haupt, Sue Ellen Haupt
Autore Haupt Randy L
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, N.J., : John Wiley, c2004
Descrizione fisica 1 online resource (273 p.)
Disciplina 519.62
Altri autori (Persone) HauptS. E
Soggetto topico Genetic algorithms
ISBN 1-280-54212-8
9786610542123
0-471-67175-4
0-471-67174-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto PRACTICAL GENETIC ALGORITHMS; CONTENTS; Preface; Preface to First Edition; List of Symbols; 1 Introduction to Optimization; 1.1 Finding the Best Solution; 1.1.1 What Is Optimization?; 1.1.2 Root Finding versus Optimization; 1.1.3 Categories of Optimization; 1.2 Minimum-Seeking Algorithms; 1.2.1 Exhaustive Search; 1.2.2 Analytical Optimization; 1.2.3 Nelder-Mead Downhill Simplex Method; 1.2.4 Optimization Based on Line Minimization; 1.3 Natural Optimization Methods; 1.4 Biological Optimization: Natural Selection; 1.5 The Genetic Algorithm; Bibliography; Exercises
2 The Binary Genetic Algorithm2.1 Genetic Algorithms: Natural Selection on a Computer; 2.2 Components of a Binary Genetic Algorithm; 2.2.1 Selecting the Variables and the Cost Function; 2.2.2 Variable Encoding and Decoding; 2.2.3 The Population; 2.2.4 Natural Selection; 2.2.5 Selection; 2.2.6 Mating; 2.2.7 Mutations; 2.2.8 The Next Generation; 2.2.9 Convergence; 2.3 A Parting Look; Bibliography; Exercises; 3 The Continuous Genetic Algorithm; 3.1 Components of a Continuous Genetic Algorithm; 3.1.1 The Example Variables and Cost Function; 3.1.2 Variable Encoding, Precision, and Bounds
3.1.3 Initial Population3.1.4 Natural Selection; 3.1.5 Pairing; 3.1.6 Mating; 3.1.7 Mutations; 3.1.8 The Next Generation; 3.1.9 Convergence; 3.2 A Parting Look; Bibliography; Exercises; 4 Basic Applications; 4.1 "Mary Had a Little Lamb"; 4.2 Algorithmic Creativity-Genetic Art; 4.3 Word Guess; 4.4 Locating an Emergency Response Unit; 4.5 Antenna Array Design; 4.6 The Evolution of Horses; 4.5 Summary; Bibliography; 5 An Added Level of Sophistication; 5.1 Handling Expensive Cost Functions; 5.2 Multiple Objective Optimization; 5.2.1 Sum of Weighted Cost Functions; 5.2.2 Pareto Optimization
5.3 Hybrid GA5.4 Gray Codes; 5.5 Gene Size; 5.6 Convergence; 5.7 Alternative Crossovers for Binary GAs; 5.8 Population; 5.9 Mutation; 5.10 Permutation Problems; 5.11 Selecting GA Parameters; 5.12 Continuous versus Binary GA; 5.13 Messy Genetic Algorithms; 5.14 Parallel Genetic Algorithms; 5.14.1 Advantages of Parallel GAs; 5.14.2 Strategies for Parallel GAs; 5.14.3 Expected Speedup; 5.14.4 An Example Parallel GA; 5.14.5 How Parallel GAs Are Being Used; Bibliography; Exercises; 6 Advanced Applications; 6.1 Traveling Salesperson Problem; 6.2 Locating an Emergency Response Unit Revisited
6.3 Decoding a Secret Message6.4 Robot Trajectory Planning; 6.5 Stealth Design; 6.6 Building Dynamic Inverse Models-The Linear Case; 6.7 Building Dynamic Inverse Models-The Nonlinear Case; 6.8 Combining GAs with Simulations-Air Pollution Receptor Modeling; 6.9 Optimizing Artificial Neural Nets with GAs; 6.10 Solving High-Order Nonlinear Partial Differential Equations; Bibliography; 7 More Natural Optimization Algorithms; 7.1 Simulated Annealing; 7.2 Particle Swarm Optimization (PSO); 7.3 Ant Colony Optimization (ACO); 7.4 Genetic Programming (GP); 7.5 Cultural Algorithms
7.6 Evolutionary Strategies
Record Nr. UNINA-9910830885403321
Haupt Randy L  
Hoboken, N.J., : John Wiley, c2004
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Stochastic Foundations in Movement Ecology : Anomalous Diffusion, Front Propagation and Random Searches / / by Vicenç Méndez, Daniel Campos, Frederic Bartumeus
Stochastic Foundations in Movement Ecology : Anomalous Diffusion, Front Propagation and Random Searches / / by Vicenç Méndez, Daniel Campos, Frederic Bartumeus
Autore Méndez Vicenç
Edizione [1st ed. 2014.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Descrizione fisica 1 online resource (320 p.)
Disciplina 519.62
Collana Springer Series in Synergetics
Soggetto topico Sociophysics
Econophysics
Ecology 
Biomathematics
Computational complexity
System theory
Data-driven Science, Modeling and Theory Building
Theoretical Ecology/Statistics
Genetics and Population Dynamics
Complexity
Complex Systems
ISBN 3-642-39010-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Elements of Probability Theory -- Introduction to Stochastic Processes -- Microscopic, Mesoscopic and Macroscopic Descriptions of Dispersal- Continuous-Time Random Walks and Anomalous Diffusion -- Reaction-Dispersal Models and Front Propagation -- Stochastic Optimal Foraging Theory -- Cell Motion and Chemotaxis -- Host-Pathogen Interactions -- Biological Invasions -- Random Search in Model Organisms.
Record Nr. UNINA-9910300386903321
Méndez Vicenç  
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Stochastic global optimization [[electronic resource] /] / by Anatoly Zhigljavsky, Antanas Žilinskas
Stochastic global optimization [[electronic resource] /] / by Anatoly Zhigljavsky, Antanas Žilinskas
Autore Zhigli͡avskiĭ A. A (Anatoliĭ Aleksandrovich)
Edizione [1st ed. 2008.]
Pubbl/distr/stampa New York, : Springer, 2008
Descrizione fisica 1 online resource (270 p.)
Disciplina 519.62
Altri autori (Persone) ZhilinskasA
Collana Springer optimization and its applications
Soggetto topico Mathematical optimization
Stochastic processes
Soggetto genere / forma Electronic books.
ISBN 1-281-13893-2
9786611138936
0-387-74740-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Basic Concepts and Ideas -- Global Random Search: Fundamentals and Statistical Inference -- Global Random Search: Extensions -- Methods Based on Statistical Models of Multimodal Functions.
Record Nr. UNINA-9910450933503321
Zhigli͡avskiĭ A. A (Anatoliĭ Aleksandrovich)  
New York, : Springer, 2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Stochastic global optimization [[electronic resource] /] / by Anatoly Zhigljavsky, Antanas Žilinskas
Stochastic global optimization [[electronic resource] /] / by Anatoly Zhigljavsky, Antanas Žilinskas
Autore Zhigli͡avskiĭ A. A (Anatoliĭ Aleksandrovich)
Edizione [1st ed. 2008.]
Pubbl/distr/stampa New York, : Springer, 2008
Descrizione fisica 1 online resource (270 p.)
Disciplina 519.62
Altri autori (Persone) ZhilinskasA
Collana Springer optimization and its applications
Soggetto topico Mathematical optimization
Stochastic processes
ISBN 1-281-13893-2
9786611138936
0-387-74740-0
Classificazione 510
SK 870
SK 880
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Basic Concepts and Ideas -- Global Random Search: Fundamentals and Statistical Inference -- Global Random Search: Extensions -- Methods Based on Statistical Models of Multimodal Functions.
Record Nr. UNINA-9910777323403321
Zhigli͡avskiĭ A. A (Anatoliĭ Aleksandrovich)  
New York, : Springer, 2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Stochastic global optimization [[electronic resource] /] / by Anatoly Zhigljavsky, Antanas Žilinskas
Stochastic global optimization [[electronic resource] /] / by Anatoly Zhigljavsky, Antanas Žilinskas
Autore Zhigli͡avskiĭ A. A (Anatoliĭ Aleksandrovich)
Edizione [1st ed. 2008.]
Pubbl/distr/stampa New York, : Springer, 2008
Descrizione fisica 1 online resource (270 p.)
Disciplina 519.62
Altri autori (Persone) ZhilinskasA
Collana Springer optimization and its applications
Soggetto topico Mathematical optimization
Stochastic processes
ISBN 1-281-13893-2
9786611138936
0-387-74740-0
Classificazione 510
SK 870
SK 880
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Basic Concepts and Ideas -- Global Random Search: Fundamentals and Statistical Inference -- Global Random Search: Extensions -- Methods Based on Statistical Models of Multimodal Functions.
Record Nr. UNINA-9910815643303321
Zhigli͡avskiĭ A. A (Anatoliĭ Aleksandrovich)  
New York, : Springer, 2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Stochastic global optimization [[electronic resource] ] : techniques and applications in chemical engineering / / editor, Gade Pandu Rangaiah
Stochastic global optimization [[electronic resource] ] : techniques and applications in chemical engineering / / editor, Gade Pandu Rangaiah
Pubbl/distr/stampa Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010
Descrizione fisica 1 online resource (722 p.)
Disciplina 519.62
Altri autori (Persone) RangaiahGade Pandu
Collana Advances in process systems engineering
Soggetto topico Chemical processes
Mathematical optimization
Stochastic processes
Chemical engineering - Mathematics
Soggetto genere / forma Electronic books.
ISBN 1-283-14433-6
9786613144331
981-4299-21-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface; CONTENTS; Chapter 1 Introduction Gade Pandu Rangaiah; 1. Optimization in Chemical Engineering; 2. Examples Requiring Global Optimization; 2.1. Modified Himmelblau function; 2.2. Ellipsoid and hyperboloid intersection; 2.3. Reactor design example; 2.4. Stepped paraboloid function; 3. Global Optimization Techniques; 4. Scope and Organization of the Book; References; Exercises; Chapter 2 Formulation and Illustration of Luus-Jaakola Optimization Procedure Rein Luus; 1. Introduction; 2. LJ Optimization Procedure; 2.1. Example of an optimization problem-diet problem with 7 foods
2.2. Example 2-Alkylation process optimization2.3. Example 3 -Gibbs free energy minimization; 3. Handling Equality Constraints; 3.1. Example 4 -Geometric problem; 3.2. Example 5 -Design of columns; 4. Effect of Parameters; 4.1. Example 7 -Minimization of Rosenbrock function; 4.2. Example 8 -Maximization of the Shubert function; 5. Conclusions; References; Exercises; Chapter 3 Adaptive Random Search and Simulated Annealing Optimizers: Algorithms and Application Issues Jacek M. Je ̇zowski, Grzegorz Poplewski and Roman Bochenek; 1. Introduction and Motivation; 2. Adaptive Random Search Approach
2.1. Introduction3. Simulated Annealing with Simplex Method; 3.1. Introduction; 3.2. SA-S/1 algorithm; 3.3. Important mechanisms of SA-S/1 algorithm; 3.3.1. Initial simplex generation; 3.3.2. Determination of the initial temperature; 3.3.3. Acceptance criterion; 3.3.4. Cooling scheme-Temperature decrease; 3.3.5. Equilibrium criterion; 3.3.6. Stopping (convergence) criterion; 4. Tests, Control Parameters Settings and Important Application Issues; 4.1. Tests-Test problems and results; 4.2. Parameter settings for SA-S/1 algorithm; 4.2.1. Cooling scheme; 4.2.2. Influence of parameter INV
4.2.3. Influence of parameter K in the equilibrium criterion4.2.4. Influence of parameter γ in the adaptive cooling scheme; 4.2.5. Influence of parameter T min; 4.3. Results and analysis of tests for LJ-MM algorithm; 4.4. Selected application issues; 4.4.1. Dealing with inequality constraints; 4.4.2. Dealing with equality constraints; 4.5. Problem size effect; 5. Summary; Symbols; Superscripts; Acronyms; References; Exercises; Appendix A; Chapter 4 Genetic Algorithms in Process Engineering: Developments and Implementation Issues Abdunnaser Younes, Ali Elkamel and Shawki Areibi
1. Introduction2. Review of Chemical Engineering Applications; 3. The Basic Genetic Algorithm; 3.1. Encoding; 3.2. Fitness evaluation; 3.3. Initial population; 3.4. Selection; 3.4.1. Fitness proportionate selection; 3.4.2. Other selection schemes; 3.5. Crossover; 3.6. Mutation; 3.7. Theoretical aspects; 3.8. General characteristics; 3.8.1. Advantages; 3.8.2. Disadvantages; 3.9. When should we use GAs?; 4. Implementation Issues; 4.1. Primary decisions; 4.1.1. Encoding; 4.2. Complex evaluations; 4.2.1. Reducing the total number of evaluations; 4.2.2. Reducing the cost of individual evaluation
4.3. Constraint handling
Record Nr. UNINA-9910456228303321
Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Stochastic global optimization [[electronic resource] ] : techniques and applications in chemical engineering / / editor, Gade Pandu Rangaiah
Stochastic global optimization [[electronic resource] ] : techniques and applications in chemical engineering / / editor, Gade Pandu Rangaiah
Pubbl/distr/stampa Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010
Descrizione fisica 1 online resource (722 p.)
Disciplina 519.62
Altri autori (Persone) RangaiahGade Pandu
Collana Advances in process systems engineering
Soggetto topico Chemical processes
Mathematical optimization
Stochastic processes
Chemical engineering - Mathematics
ISBN 1-283-14433-6
9786613144331
981-4299-21-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Preface; CONTENTS; Chapter 1 Introduction Gade Pandu Rangaiah; 1. Optimization in Chemical Engineering; 2. Examples Requiring Global Optimization; 2.1. Modified Himmelblau function; 2.2. Ellipsoid and hyperboloid intersection; 2.3. Reactor design example; 2.4. Stepped paraboloid function; 3. Global Optimization Techniques; 4. Scope and Organization of the Book; References; Exercises; Chapter 2 Formulation and Illustration of Luus-Jaakola Optimization Procedure Rein Luus; 1. Introduction; 2. LJ Optimization Procedure; 2.1. Example of an optimization problem-diet problem with 7 foods
2.2. Example 2-Alkylation process optimization2.3. Example 3 -Gibbs free energy minimization; 3. Handling Equality Constraints; 3.1. Example 4 -Geometric problem; 3.2. Example 5 -Design of columns; 4. Effect of Parameters; 4.1. Example 7 -Minimization of Rosenbrock function; 4.2. Example 8 -Maximization of the Shubert function; 5. Conclusions; References; Exercises; Chapter 3 Adaptive Random Search and Simulated Annealing Optimizers: Algorithms and Application Issues Jacek M. Je ̇zowski, Grzegorz Poplewski and Roman Bochenek; 1. Introduction and Motivation; 2. Adaptive Random Search Approach
2.1. Introduction3. Simulated Annealing with Simplex Method; 3.1. Introduction; 3.2. SA-S/1 algorithm; 3.3. Important mechanisms of SA-S/1 algorithm; 3.3.1. Initial simplex generation; 3.3.2. Determination of the initial temperature; 3.3.3. Acceptance criterion; 3.3.4. Cooling scheme-Temperature decrease; 3.3.5. Equilibrium criterion; 3.3.6. Stopping (convergence) criterion; 4. Tests, Control Parameters Settings and Important Application Issues; 4.1. Tests-Test problems and results; 4.2. Parameter settings for SA-S/1 algorithm; 4.2.1. Cooling scheme; 4.2.2. Influence of parameter INV
4.2.3. Influence of parameter K in the equilibrium criterion4.2.4. Influence of parameter γ in the adaptive cooling scheme; 4.2.5. Influence of parameter T min; 4.3. Results and analysis of tests for LJ-MM algorithm; 4.4. Selected application issues; 4.4.1. Dealing with inequality constraints; 4.4.2. Dealing with equality constraints; 4.5. Problem size effect; 5. Summary; Symbols; Superscripts; Acronyms; References; Exercises; Appendix A; Chapter 4 Genetic Algorithms in Process Engineering: Developments and Implementation Issues Abdunnaser Younes, Ali Elkamel and Shawki Areibi
1. Introduction2. Review of Chemical Engineering Applications; 3. The Basic Genetic Algorithm; 3.1. Encoding; 3.2. Fitness evaluation; 3.3. Initial population; 3.4. Selection; 3.4.1. Fitness proportionate selection; 3.4.2. Other selection schemes; 3.5. Crossover; 3.6. Mutation; 3.7. Theoretical aspects; 3.8. General characteristics; 3.8.1. Advantages; 3.8.2. Disadvantages; 3.9. When should we use GAs?; 4. Implementation Issues; 4.1. Primary decisions; 4.1.1. Encoding; 4.2. Complex evaluations; 4.2.1. Reducing the total number of evaluations; 4.2.2. Reducing the cost of individual evaluation
4.3. Constraint handling
Record Nr. UNINA-9910780885703321
Singapore ; ; Hackensack, N.J., : World Scientific Pub. Co., 2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui