Swarm intelligence and bio-inspired computation : theory and applications /

Mostrar otras versiones (3)

Annotation

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Yang, Xin-She (Editor ), Cui, Zhihua (Editor ), Xiao, Renbin (Editor ), Gandomi, Amir Hossein (Editor ), Karamanoglu, Mehmet (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam ; Boston : Elsevier, 2013.
Colección:Elsevier insights.
Temas:
Swarm intelligence.
Natural computation.
Calcul naturel.
Schwarmintelligenz
Biocomputer
Bioinformatik
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Machine generated contents note: 1. Swarm Intelligence and Bio-Inspired Computation: An Overview
  • 1.1. Introduction / Mehmet Karamanoglu / Xin-She Yang
  • 1.2. Current Issues in Bio-Inspired Computing / Mehmet Karamanoglu / Xin-She Yang
  • 1.2.1. Gaps Between Theory and Practice / Mehmet Karamanoglu / Xin-She Yang
  • 1.2.2. Classifications and Terminology / Mehmet Karamanoglu / Xin-She Yang
  • 1.2.3. Tuning of Algorithm-Dependent Parameters / Mehmet Karamanoglu / Xin-She Yang
  • 1.2.4. Necessity for Large-Scale and Real-World Applications / Mehmet Karamanoglu / Xin-She Yang
  • 1.2.5. Choice of Algorithms / Mehmet Karamanoglu / Xin-She Yang
  • 1.3. Search for the Magic Formulas for Optimization / Mehmet Karamanoglu / Xin-She Yang
  • 1.3.1. Essence of an Algorithm / Mehmet Karamanoglu / Xin-She Yang
  • 1.3.2. What Is an Ideal Algorithm? / Mehmet Karamanoglu / Xin-She Yang
  • 1.3.3. Algorithms and Self-Organization / Mehmet Karamanoglu / Xin-She Yang
  • 1.3.4. Links Between Algorithms and Self-Organization / Mehmet Karamanoglu / Xin-She Yang
  • 1.3.5. The Magic Formulas / Mehmet Karamanoglu / Xin-She Yang
  • 1.4. Characteristics of Metaheuristics / Mehmet Karamanoglu / Xin-She Yang
  • 1.4.1. Intensification and Diversification / Mehmet Karamanoglu / Xin-She Yang
  • 1.4.2. Randomization Techniques / Mehmet Karamanoglu / Xin-She Yang
  • 1.5. Swarm-Intelligence-Based Algorithms / Mehmet Karamanoglu / Xin-She Yang
  • 1.5.1. Ant Algorithms / Mehmet Karamanoglu / Xin-She Yang
  • 1.5.2. Bee Algorithms / Mehmet Karamanoglu / Xin-She Yang
  • 1.5.3. Bat Algorithm / Mehmet Karamanoglu / Xin-She Yang
  • 1.5.4. Particle Swarm Optimization / Mehmet Karamanoglu / Xin-She Yang
  • 1.5.5. Firefly Algorithm / Mehmet Karamanoglu / Xin-She Yang
  • 1.5.6. Cuckoo Search / Mehmet Karamanoglu / Xin-She Yang
  • 1.5.7. Flower Pollination Algorithm / Mehmet Karamanoglu / Xin-She Yang
  • 1.5.8. Other Algorithms / Mehmet Karamanoglu / Xin-She Yang
  • 1.6. Open Problems and Further Research Topics / Mehmet Karamanoglu / Xin-She Yang
  • References / Mehmet Karamanoglu / Xin-She Yang
  • 2. Analysis of Swarm Intelligence-Based Algorithms for Constrained Optimization / Mehmet Karamanoglu / Xin-She Yang
  • 2.1. Introduction / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.2. Optimization Problems / E. Dogan / M.P. Saka / Ibrahim Aydogdu
  • 2.3. Swarm Intelligence-Based Optimization Algorithms / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.3.1. Ant Colony Optimization / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.3.2. Particle Swarm Optimizer / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.3.3. ABC Algorithm / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.3.4. Glowworm Swarm Algorithm / M.P. Saka / E. Dogan / Ibrahim Aydogdu
  • 2.3.5. Firefly Algorithm / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.3.6. Cuckoo Search Algorithm / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.3.7. Bat Algorithm / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.3.8. Hunting Search Algorithm / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.4. Numerical Examples / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.4.1. Example 1 / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.4.2. Example 2 / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 2.5. Summary and Conclusions / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • References / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 3. L�evy Flights and Global Optimization / M.P. Saka / Ibrahim Aydogdu / E. Dogan
  • 3.1. Introduction / Hans-J�urgen Zepernick / Momin Jamil
  • 3.2. Metaheuristic Algorithms / Hans-J�urgen Zepernick / Momin Jamil
  • 3.3. L�evy Flights in Global Optimization / Hans-J�urgen Zepernick / Momin Jamil
  • 3.3.1. The L�evy Probability Distribution / Hans-J�urgen Zepernick / Momin Jamil
  • 3.3.2. Simulation of L�evy Random Numbers / Hans-J�urgen Zepernick / Momin Jamil
  • 3.3.3. Diversification and Intensification / Hans-J�urgen Zepernick / Momin Jamil
  • 3.4. Metaheuristic Algorithms Based on L�evy Probability Distribution: Is It a Good Idea? / Hans-J�urgen Zepernick / Momin Jamil
  • 3.4.1. Evolutionary Programming Using Mutations Based on the L�evy Probability Distribution / Hans-J�urgen Zepernick / Momin Jamil
  • 3.4.2. L�evy Particle Swarm / Momin Jamil / Hans-J�urgen Zepernick
  • 3.4.3. Cuckoo Search / Hans-J�urgen Zepernick / Momin Jamil
  • 3.4.4. Modified Cuckoo Search / Hans-J�urgen Zepernick / Momin Jamil
  • 3.4.5. Firefly Algorithm / Hans-J�urgen Zepernick / Momin Jamil
  • 3.4.6. Eagle Strategy / Hans-J�urgen Zepernick / Momin Jamil
  • 3.5. Discussion / Hans-J�urgen Zepernick / Momin Jamil
  • 3.6. Conclusions / Hans-J�urgen Zepernick / Momin Jamil
  • References / Hans-J�urgen Zepernick / Momin Jamil
  • 4. Memetic Self-Adaptive Firefly Algorithm / Hans-J�urgen Zepernick / Momin Jamil
  • 4.1. Introduction / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 4.2. Optimization Problems and Their Complexity / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 4.3. Memetic Self-Adaptive Firefly Algorithm / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 4.3.1. Self-Adaptation of Control Parameters / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 4.3.2. Population Model / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 4.3.3. Balancing Between Exploration and Exploitation / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 4.3.4. The Local Search / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 4.3.5. Scheme of the MSA-FFA / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 4.4. Case Study: Graph 3-Coloring / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 4.4.1. Graph 3-Coloring / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 4.4.2. MSA-FFA for Graph 3-Coloring / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 4.4.3. Experiments and Results / Iztok Jr. Fister / Janez Brest / Xin-She Yang / Iztok Fister
  • 4.5. Conclusions / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • References / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang.
  • 5. Modeling and Simulation of Ant Colony's Labor Division: A Problem-Oriented Approach / Iztok Fister / Iztok Jr. Fister / Janez Brest / Xin-She Yang
  • 5.1. Introduction / Renbin Xiao
  • 5.2. Ant Colony's Labor Division Behavior and its Modeling Description / Renbin Xiao
  • 5.2.1. Ant Colony's Labor Division / Renbin Xiao
  • 5.2.2. Ant Colony's Labor Division Model / Renbin Xiao
  • 5.2.3. Some Analysis / Renbin Xiao
  • 5.3. Modeling and Simulation of Ant Colony's Labor Division with Multitask / Renbin Xiao
  • 5.3.1. Background Analysis / Renbin Xiao
  • 5.3.2. Design and Implementation of Ant Colony's Labor Division Model with Multitask / Renbin Xiao
  • 5.3.3. Supply Chain Virtual Enterprise Simulation / Renbin Xiao
  • 5.3.4. Virtual Organization Enterprise Simulation / Renbin Xiao
  • 5.3.5. Discussion / Renbin Xiao
  • 5.4. Modeling and Simulation of Ant Colony's Labor Division with Multistate / Renbin Xiao
  • 5.4.1. Background Analysis / Renbin Xiao
  • 5.4.2. Design and Implementation of Ant Colony's Labor Division Model with Multistate / Renbin Xiao
  • 5.4.3. Simulation Example of Ant Colony's Labor Division Model with Multistate / Renbin Xiao
  • 5.5. Modeling and Simulation of Ant Colony's Labor Division with Multiconstraint / Renbin Xiao
  • 5.5.1. Background Analysis / Renbin Xiao
  • 5.5.2. Design and Implementation of Ant Colony's Labor Division Model with Multiconstraint / Renbin Xiao
  • 5.5.3. Simulation Results and Analysis / Renbin Xiao
  • 5.6. Concluding Remarks / Renbin Xiao
  • Acknowledgment / Renbin Xiao
  • References / Renbin Xiao
  • 6. Particle Swarm Algorithm: Convergence and Applications / Renbin Xiao
  • 6.1. Introduction / Shichang Sun / Hongbo Liu
  • 6.2. Convergence Analysis / Shichang Sun / Hongbo Liu
  • 6.2.1. Individual Trajectory / Shichang Sun / Hongbo Liu
  • 6.2.2. Probabilistic Analysis / Shichang Sun / Hongbo Liu
  • 6.3. Performance Illustration / Shichang Sun / Hongbo Liu
  • 6.3.1. Dataflow Application / Shichang Sun / Hongbo Liu
  • 6.4. Application in Hidden Markov Models / Shichang Sun / Hongbo Liu
  • 6.4.1. Parameters Weighted HMM / Shichang Sun / Hongbo Liu
  • 6.4.2. PSO-Viterbi for Parameters Weighted HMMs / Shichang Sun / Hongbo Liu
  • 6.4.3. POS Tagging Problem and Solution / Shichang Sun / Hongbo Liu
  • 6.4.4. Experiment / Shichang Sun / Hongbo Liu
  • 6.5. Conclusions / Shichang Sun / Hongbo Liu
  • References / Shichang Sun / Hongbo Liu
  • 7.A Survey of Swarm Algorithms Applied to Discrete Optimization Problems / Shichang Sun / Hongbo Liu
  • 7.1. Introduction / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2. Swarm Algorithms / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.1. Particle Swarm Optimization / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.2. Roach Infestation Optimization / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.3. Cuckoo Search Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.4. Firefly Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.5. Gravitational Search Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.6. Bat Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.7. Glowworm Swarm Optimization Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.8. Artificial Fish School Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.9. Bacterial Evolutionary Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.10. Bee Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes.
  • Note continued: 7.2.11. Artificial Bee Colony Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.12. Bee Colony Optimization / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.2.13. Marriage in Honey-Bees Optimization Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.3. Main Concerns to Handle Discrete Problems / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.3.1. Discretization Methods / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.4. Applications to Discrete Problems / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.4.1. Particle Swarm Optimization / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.4.2. Roach Infestation Optimization / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.4.3. Cuckoo Search Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.4.4. Firefly Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.4.5. Bee Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.4.6. Artificial Bee Colony / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.4.7. Bee Colony Optimization / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.4.8. Marriage in Honey-Bees Optimization Algorithm / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.4.9. Other Swarm Intelligence Algorithms / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.5. Discussion / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 7.6. Concluding Remarks and Future Research / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • References / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 8. Test Functions for Global Optimization: A Comprehensive Survey / Jonas Krause / Jelson Cordeiro / Rafael Stubs Parpinelli / Heitor Silv�erio Lopes
  • 8.1. Introduction / Momin Jamil / Xin-She Yang / Hans-J�urgen Zepernick
  • 8.2.A Collection of Test Functions for GO / Momin Jamil / Xin-She Yang / Hans-J�urgen Zepernick
  • 8.2.1. Unimodal Test Functions / Momin Jamil / Xin-She Yang / Hans-J�urgen Zepernick
  • 8.2.2. Multimodal Function / Momin Jamil / Xin-She Yang / Hans-J�urgen Zepernick
  • 8.3. Conclusions / Momin Jamil / Xin-She Yang / Hans-J�urgen Zepernick
  • References / Momin Jamil / Xin-She Yang / Hans-J�urgen Zepernick
  • 9. Binary Bat Algorithm for Feature Selection / Momin Jamil / Xin-She Yang / Hans-J�urgen Zepernick
  • 9.1. Introduction / Rodrigo Yuji Mizobe Nakamura / Luzs Augusto Martins Pereira / Douglas Rodrigues / Kelton Augusto Pontara Costa / Jo�ao Paulo Papa / Xin-She Yang
  • 9.2. Bat Algorithm / Xin-She Yang / Rodrigo Yuji Mizobe Nakamura / Luzs Augusto Martins Pereira / Douglas Rodrigues / Kelton Augusto Pontara Costa / Jo�ao Paulo Papa
  • 9.3. Binary Bat Algorithm / Rodrigo Yuji Mizobe Nakamura / Luzs Augusto Martins Pereira / Douglas Rodrigues / Kelton Augusto Pontara Costa / Jo�ao Paulo Papa / Xin-She Yang
  • 9.4. Optimum-Path Forest Classifier / Rodrigo Yuji Mizobe Nakamura / Luzs Augusto Martins Pereira / Xin-She Yang / Kelton Augusto Pontara Costa / Jo�ao Paulo Papa / Douglas Rodrigues
  • 9.4.1. Background Theory / Rodrigo Yuji Mizobe Nakamura / Luzs Augusto Martins Pereira / Xin-She Yang / Kelton Augusto Pontara Costa / Jo�ao Paulo Papa / Douglas Rodrigues
  • 9.5. Binary Bat Algorithm / Rodrigo Yuji Mizobe Nakamura / Luzs Augusto Martins Pereira / Xin-She Yang / Kelton Augusto Pontara Costa / Jo�ao Paulo Papa / Douglas Rodrigues
  • 9.6. Experimental Results / Rodrigo Yuji Mizobe Nakamura / Luzs Augusto Martins Pereira / Xin-She Yang / Kelton Augusto Pontara Costa / Jo�ao Paulo Papa / Douglas Rodrigues
  • 9.7. Conclusions / Rodrigo Yuji Mizobe Nakamura / Luzs Augusto Martins Pereira / Xin-She Yang / Kelton Augusto Pontara Costa / Jo�ao Paulo Papa / Douglas Rodrigues
  • References / Rodrigo Yuji Mizobe Nakamura / Luzs Augusto Martins Pereira / Xin-She Yang / Kelton Augusto Pontara Costa / Jo�ao Paulo Papa / Douglas Rodrigues
  • 10. Intelligent Music Composition / Rodrigo Yuji Mizobe Nakamura / Luzs Augusto Martins Pereira / Xin-She Yang / Kelton Augusto Pontara Costa / Jo�ao Paulo Papa / Douglas Rodrigues
  • 10.1. Introduction / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis / Andreas Floros
  • 10.2. Unsupervised Intelligent Composition / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis / Andreas Floros
  • 10.2.1. Unsupervised Composition with Cellular Automata / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis / Andreas Floros
  • 10.2.2. Unsupervised Composition with L-Systems / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis / Andreas Floros
  • 10.3. Supervised Intelligent Composition / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis / Andreas Floros
  • 10.3.1. Supervised Composition with Genetic Algorithms / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis / Andreas Floros
  • 10.3.2. Supervised Composition Genetic Programming / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis / Andreas Floros
  • 10.4. Interactive Intelligent Composition / Andreas Floros / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis
  • 10.4.1.Composing with Swarms / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis / Andreas Floros
  • 10.4.2. Interactive Composition with GA and GP / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis / Andreas Floros
  • 10.5. Conclusions / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis / Andreas Floros
  • References / Maximos A. Kaliakatsos-Papakostas / Michael N. Vrahatis / Andreas Floros.
  • 11.A Review of the Development and Applications of the Cuckoo Search Algorithm / Maximos A. Kaliakatsos-Papakostas / Andreas Floros / Michael N. Vrahatis
  • 11.1. Introduction / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.2. Cuckoo Search Algorithm / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.2.1. The Analogy / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.2.2. Cuckoo Breeding Behavior / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.2.3. L�evy Flights / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.2.4. The Algorithm / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.2.5. Validation / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.3. Modifications and Developments / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.3.1. Algorithmic Modifications / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.3.2. Hybridization / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.4. Applications / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.4.1. Applications in Machine Learning / M. Rowan Brown / Kenneth Morgan / Oubay Hassan / Sean Walton
  • 11.4.2. Applications in Design / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 11.5. Conclusion / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • References / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 12. Bio-Inspired Models for Semantic Web / Sean Walton / M. Rowan Brown / Kenneth Morgan / Oubay Hassan
  • 12.1. Introduction / Rajendra Akerkar / Priti Srinivas Sajja
  • 12.2. Semantic Web / Rajendra Akerkar / Priti Srinivas Sajja
  • 12.3. Constituent Models / Priti Srinivas Sajja / Rajendra Akerkar
  • 12.3.1. Artificial Neural Network / Priti Srinivas Sajja / Rajendra Akerkar
  • 12.3.2. Genetic Algorithms / Rajendra Akerkar / Priti Srinivas Sajja
  • 12.3.3. Swarm Intelligence / Rajendra Akerkar / Priti Srinivas Sajja
  • 12.3.4. Application in Different Aspects of Semantic Web / Rajendra Akerkar / Priti Srinivas Sajja
  • 12.4. Neuro-Fuzzy System for the Web Content Filtering: Application / Rajendra Akerkar / Priti Srinivas Sajja
  • 12.5. Conclusions / Rajendra Akerkar / Priti Srinivas Sajja
  • References / Rajendra Akerkar / Priti Srinivas Sajja
  • 13. Discrete Firefly Algorithm for Traveling Salesman Problem: A New Movement Scheme / Rajendra Akerkar / Priti Srinivas Sajja
  • 13.1. Introduction / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.2. Evolutionary Discrete Firefly Algorithm / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.2.1. The Representation of the Firefly / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.2.2. Light Intensity / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.2.3. Distance / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.2.4. Attractiveness / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.2.5. Light Absorption / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.2.6. Movement / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.2.7. Inversion Mutation / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.2.8. EDFA Scheme / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.3.A New DFA for the TSP / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.3.1. Edge-Based Movement / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.3.2. New DFA Scheme / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.4. Result and Discussion / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.4.1. Firefly Population / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.4.2. Effect of Light Absorption / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.4.3. Number of Updating Index / Suyanto / Ruli Manurung / Gilang Kusuma Jati
  • 13.4.4. Performance of New DFA / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 13.5. Conclusion / Gilang Kusuma Jati / Suyant.
  • Note continued: Acknowledgment / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • References / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 14. Modeling to Generate Alternatives Using Biologically Inspired Algorithms / Gilang Kusuma Jati / Suyanto / Ruli Manurung
  • 14.1. Introduction / Julian Scott Yeomans / Raha Imanirad
  • 14.2. Modeling to Generate Alternatives / Julian Scott Yeomans / Raha Imanirad
  • 14.3. FA for Function Optimization / Julian Scott Yeomans / Raha Imanirad
  • 14.4. FA-Based Concurrent Coevolutionary Computational Algorithm for MGA / Julian Scott Yeomans / Raha Imanirad
  • 14.5.Computational Testing of the FA Used for MGA / Julian Scott Yeomans / Raha Imanirad
  • 14.6. An SO Approach for Stochastic MGA / Julian Scott Yeomans / Raha Imanirad
  • 14.7. Case Study of Stochastic MGA for the Expansion of Waste Management Facilities / Julian Scott Yeomans / Raha Imanirad
  • 14.8. Conclusions / Julian Scott Yeomans / Raha Imanirad
  • References / Julian Scott Yeomans / Raha Imanirad
  • 15. Structural Optimization Using Krill Herd Algorithm / Julian Scott Yeomans / Raha Imanirad
  • 15.1. Introduction / Amir Hossein Gandomi / Siamak Talatahari / Amir Hossein Alavi
  • 15.2. Krill Herd. Algorithm / Amir Hossein Gandomi / Siamak Talatahari / Amir Hossein Alavi
  • 15.2.1. Lagrangian Model of Krill Herding / Amir Hossein Gandomi / Siamak Talatahari / Amir Hossein Alavi
  • 15.3. Implementation and Numerical Experiments / Amir Hossein Gandomi / Siamak Talatahari / Amir Hossein Alavi
  • 15.3.1. Case I: Structural Design of a Pin-Jointed Plane Frame / Amir Hossein Gandomi / Siamak Talatahari / Amir Hossein Alavi
  • 15.3.2. Case II: A Reinforced Concrete Beam Design / Amir Hossein Gandomi / Siamak Talatahari / Amir Hossein Alavi
  • 15.3.3. Case III: 25-Bar Space Truss Design / Amir Hossein Gandomi / Siamak Talatahari / Amir Hossein Alavi
  • 15.4. Conclusions and Future Research / Amir Hossein Gandomi / Siamak Talatahari / Amir Hossein Alavi
  • References / Amir Hossein Gandomi / Siamak Talatahari / Amir Hossein Alavi
  • 16. Artificial Plant Optimization Algorithm / Amir Hossein Alavi / Amir Hossein Gandomi / Siamak Talatahari
  • 16.1. Introduction / Xingjuan Cai / Zhihua Cui
  • 16.2. Primary APOA / Xingjuan Cai / Zhihua Cui
  • 16.2.1. Main Method / Xingjuan Cai / Zhihua Cui
  • 16.2.2. Photosynthesis Operator / Xingjuan Cai / Zhihua Cui
  • 16.2.3. Phototropism Operator / Xingjuan Cai / Zhihua Cui
  • 16.2.4. Applications to Artificial Neural Network Training / Xingjuan Cai / Zhihua Cui
  • 16.3. Standard APOA / Xingjuan Cai / Zhihua Cui
  • 16.3.1. Drawbacks of PAPOA / Xingjuan Cai / Zhihua Cui
  • 16.3.2. Phototropism Operator / Zhihua Cui / Xingjuan Cai
  • 16.3.3. Apical Dominance Operator / Xingjuan Cai / Zhihua Cui
  • 16.3.4. Application to Toy Model of Protein Folding / Xingjuan Cai / Zhihua Cui
  • 16.4. Conclusion / Xingjuan Cai / Zhihua Cui
  • Acknowledgment / Xingjuan Cai / Zhihua Cui
  • References / Xingjuan Cai / Zhihua Cui.
  • 17. Genetic Algorithm for the Dynamic Berth Allocation Problem in Real Time / Xingjuan Cai / Zhihua Cui
  • 17.1. Introduction / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.2. Literature Review / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.3. Optimization Model / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.3.1. Sets / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.3.2. Parameters / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.3.3. Decision Variables / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.4. Solution Procedure by Genetic Algorithm / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.4.1. Representation / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.4.2. Fitness / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.4.3. Selection of Parents and Genetic Operators / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.4.4. Mutation / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.4.5. Crossover / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.5. Results and Analysis / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 17.6. Conclusion / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • References / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 18. Opportunities and Challenges of Integrating Bio-Inspired Optimization and Data Mining Algorithms / Carlos Arango / Luis Onieva / Alejandro Escudero / Pablo Cort�es
  • 18.1. Introduction / Simon Fong
  • 18.2. Challenges in Data Mining / Simon Fong
  • 18.2.1. Curse of Dimensionality / Simon Fong
  • 18.2.2. Data Streaming / Simon Fong
  • 18.3. Bio-Inspired Optimization Metaheuristics / Simon Fong
  • 18.4. The Convergence / Simon Fong
  • 18.4.1. Integrating BiCam Algorithms into Clustering / Simon Fong
  • 18.4.2. Integrating BiCam Algorithms into Feature Selection / Simon Fong
  • 18.5. Conclusion / Simon Fong
  • References / Simon Fong
  • 19. Improvement of PSO Algorithm by Memory-Based Gradient Search-Application in Inventory Management / Simon Fong
  • 19.1. Introduction / Tam�ar Varga / J�anos Abonyi / Andr�as Kir�aly
  • 19.2. The Improved PSO Algorithm / Tam�ar Varga / J�anos Abonyi / Andr�as Kir�aly
  • 19.2.1. Classical PSO Algorithm / Tam�ar Varga / J�anos Abonyi / Andr�as Kir�aly
  • 19.2.2. Improved PSO Algorithm / Tam�ar Varga / J�anos Abonyi / Andr�as Kir�aly
  • 19.2.3. Results / Tam�ar Varga / J�anos Abonyi / Andr�as Kir�aly
  • 19.3. Stochastic Optimization of Multiechelon Supply Chain Model / Tam�ar Varga / J�anos Abonyi / Andr�as Kir�aly
  • 19.3.1. Inventory Model of a Single Warehouse / Tam�ar Varga / J�anos Abonyi / Andr�as Kir�aly
  • 19.3.2. Inventory Model of a Supply Chain / Tam�ar Varga / J�anos Abonyi / Andr�as Kir�aly
  • 19.3.3. Optimization Results / Tam�ar Varga / Andr�as Kir�aly / J�anos Abonyi
  • 19.4. Conclusion / Tam�ar Varga / Andr�as Kir�aly / J�anos Abonyi
  • Acknowledgment / Tam�ar Varga / Andr�as Kir�aly / J�anos Abonyi.

Ejemplares similares