Self-Organizing Migrating Algorithm : Methodology and Implementation / / edited by Donald Davendra, Ivan Zelinka

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
• Edizione: 1st ed. 2016.
• Descrizione fisica: 1 online resource (XVIII, 289 p. 128 illus., 41 illus. in color.)
• Disciplina: 006.3
• Soggetto topico: Computational intelligence; Artificial intelligence; Mathematical optimization; Computational Intelligence; Artificial Intelligence; Optimization
• Persona (resp. second.): DavendraDonald; ZelinkaIvan
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di contenuto: Intro -- Foreword -- Preface -- Organization of the Chapters -- Acknowledgments -- Contents -- Contributors -- Part I Methodology -- 1 SOMA---Self-organizing Migrating Algorithm -- Abstract -- 1 Introduction -- 2 Historical Background and Algorithm Classification -- 2.1 SOMA in the Context of Selected Evolutionary Algorithms -- 3 SOMA Applicability -- 4 SOMA Principles and Control Parameters -- 5 SOMA Strategies -- 5.1 SOMA Parameters -- 5.2 Standard Evolutionary Operations in SOMA -- 5.2.1 Population -- 5.2.2 Mutations -- 5.2.3 Crossover -- 5.2.4 Constraint Handling -- 5.2.5 Boundary Constraints -- 5.2.6 Constraint Functions -- 5.2.7 Handling of Integer and Discrete Variables -- 6 Parameter Dependence -- 7 SOMA and Cost Function Evaluations -- 8 Selected SOMA Applications -- 9 SOMA in Computer Games -- 10 SOMA and Interdisciplinary Research -- 11 Conclusion -- Acknowledgments -- References -- 2 DSOMA---Discrete Self Organising Migrating Algorithm -- Abstract -- 1 Introduction -- 2 Discrete Self-organising Migrating Algorithm -- 3 Initialisation -- 4 Creating Jump Sequences -- 5 Constructing Trial Individuals -- 6 Repairing Trial Individuals -- 7 Population Update -- 8 Iteration -- 9 Migrations -- 10 2 Opt Local Search -- 11 Conclusion -- Acknowledgments -- References -- Part II Implementation -- 3 SOMA and Strange Dynamics -- Abstract -- 1 Introduction -- 2 SOMA and Chaos -- 2.1 Chaos Synthesis -- 2.2 Chaos Control -- 2.3 Chaos Identification -- 2.4 SOMA Powered by Pseudorandom, Chaos and Deterministic Dynamics -- 3 SOMA and Fractal Geometry -- 4 SOMA Dynamics as a Complex Networks -- 5 Conclusion -- Acknowledgment -- References -- 4 Multi-objective Self-organizing Migrating Algorithm -- Abstract -- 1 Introduction to Multi-objective Optimization -- 2 MOSOMA -- 2.1 Controlling Parameters -- 2.2 Migration of Agents. ; 2.3 Final Non-dominated Set Choice -- 3 Appendix I---Evaluation Metrics -- 4 Appendix II---Benchmark Problems -- Acknowledgements -- References -- 5 Multi-objective Design of EM Components -- Abstract -- 1 Design of EM Components -- 1.1 Yagi-Uda Antenna Design -- 1.2 Dielectric Layered Filter Design -- 1.3 Adaptive Beamforming in Time Domain -- Acknowledgements -- References -- 6 Utilization of Parallel Computing for Discrete Self-organizing Migration Algorithm -- Abstract -- 1 Introduction -- 2 Levels of Parallelization -- 3 Hardware and Software Options for Parallelization -- 3.1 OpenMP -- 3.2 Message Passing Interface -- 3.2.1 Brief Introduction into Kaira -- 3.3 GPU Computing with CUDA -- 4 Parallelization of DSOMA -- 4.1 OpenMP Implementation of DSOMA -- 4.2 Distributed Island Model Implementation of DSOMA -- 4.3 GPU Implementation -- 4.3.1 Data Storage, Transfers and Alignment -- 4.3.2 Data Level Prallelism -- 4.3.3 Single Thread Computation -- 4.3.4 Block/Warp Computation -- 5 Experiments -- 5.1 OpenMP Experiments -- 5.2 CUDA Experiments -- 5.3 Distributed DSOMA Experiment -- 6 Conclusion -- Acknowledgements -- References -- 7 C-SOMAQI: Self Organizing Migrating Algorithm with Quadratic Interpolation Crossover Operator for Constrained Global Optimization -- Abstract -- 1 Introduction -- 2 Preliminaries -- 2.1 Self Organizing Migrating Algorithm -- 2.1.1 Mutation: Perturbation -- 2.1.2 Crossover Operator: Generation of New Positions of Individuals -- 2.2 Quadratic Interpolation (QI) Crossover Operator -- 2.3 Methodology of SOMAQI -- 3 Proposed Hybrid C-SOMAQI Algorithm -- 4 Numerical Results on Benchmark Problems -- 5 Conclusions -- Appendix -- References -- 8 Optimization of Directional Overcurrent Relay Times Using C-SOMGA -- Abstract -- 1 Introduction -- 2 Previous Work -- 3 Methodology -- 3.1 Methodology of C-SOMGA. ; 4 General form of the Problem -- 5 The Optimization Problem -- 6 Model I (The IEEE 3-Bus Model) -- 7 Model II (The IEEE 4-Bus Model) -- 8 Model III (The IEEE 6-Bus Model) -- 9 Method of Solution and Discussion of Results -- 10 Conclusions -- References -- 9 SOMGA for Large Scale Function Optimization and Its Application -- Abstract -- 1 Introduction -- 2 Previous Work Done -- 3 Section 1 -- 3.1 Solution of Large Scale Problems Using SOMGA -- 3.2 Results and Discussion -- 4 Section 2 -- 4.1 Optimal Thermohydraulic Performance of an Artificially Roughened Air Heater -- 4.2 Frequency Modulation Sounds Parameter Identification Problem -- 5 Section 3 -- 5.1 Comparison with the Memetic Algorithm -- 5.2 Results and Discussion -- 6 Conclusions -- Appendix -- References -- 10 Solving the Routing Problems with Time Windows -- Abstract -- 1 Introduction -- 2 Mathematical Models of Selected Routing Problems -- 2.1 Traveling Salesman Problem (TSP) -- 2.2 Traveling Salesman Problem with Time Windows (TSPTW) -- 2.3 Capacitated Vehicle Routing Problem (CVRP) -- 2.4 Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) -- 3 Self-organizing Migrating Algorithm for the Routing Problems -- 4 Computational Experiments -- 5 Solving the Real-Life Vehicle Routing Problem with Time Windows -- 6 Conclusion -- References -- 11 SOMA in Financial Modeling -- Abstract -- 1 Introduction -- 2 Portfolio Theory -- 2.1 Markowitz Model -- 2.2 Portfolio Performance Measurement Techniques -- 2.2.1 Portfolio Selection by Maximizing Sortino Ratio -- 2.2.2 Portfolio Selection by Maximizing Omega Function -- 3 SOMA for Maximizing Performance Measurement -- 4 Empirical Results -- 5 Conclusions -- References -- 12 Setting of Control Parameters of SOMA on the Base of Statistics -- Abstract -- 1 Introduction -- 2 Single and Multiple-Factor Analysis of Variance---Theory. ; 3 Parameters Setting of SOMA -- 4 Conclusions -- References -- 13 Inspired in SOMA: Perturbation Vector Embedded into the Chaotic PSO Algorithm Driven by Lozi Chaotic Map -- Abstract -- 1 Introduction -- 2 Motivation -- 3 Particle Swarm Optimization Algorithm -- 4 Self-organizing Migrating Algorithm (SOMA) -- 5 Lozi Chaotic Map -- 6 Test Functions -- 7 Experiment Setup -- 7.1 Notation -- 8 Results -- 9 Analysis of the Results -- 10 Conclusion -- Acknowledgments -- References.
• Sommario/riassunto: This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA. .
• Titolo autorizzato: Self-Organizing Migrating Algorithm
• ISBN: 3-319-28161-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910253962903321
• Lo trovi qui: Univ. Federico II
• Serie: Studies in Computational Intelligence, . 1860-949X ; ; 626