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Advances in Metaheuristics

Advances in Metaheuristics
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Feldname Details
Vorliegende Sprache eng
Hinweise auf parallele Ausgaben 382853202 Buchausg. u.d.T.: ‡Advances in metaheuristics
ISBN 978-1-4614-6321-4
Name Di Gaspero, Luca
Schaerf, Andrea
Name ANZEIGE DER KETTE Schaerf, Andrea
Name Stützle, Thomas
T I T E L Advances in Metaheuristics
Verlagsort New York, NY
Verlag Springer
Erscheinungsjahr 2013
2013
Umfang Online-Ressource (XII, 183 p. 60 illus., 19 illus. in color, digital)
Reihe Operations Research/Computer Science Interfaces Series ; 53
Notiz / Fußnoten Description based upon print version of record
Weiterer Inhalt Advances in Metaheuristics; Preface; Acknowledgements; Contents; Finite First Hitting Time Versus Stochastic Convergence in Particle Swarm Optimisation; 1 Introduction; 2 Preliminaries; 3 Stagnation; 4 Mean Square Convergence; 5 Noisy PSO; 6 Experiments; 7 Conclusions; References; Using Performance Profiles for the Analysis and Design of Benchmark Experiments; 1 Introduction; 2 Performance Profiles; 3 Performance Profiles Variants; 3.1 A Multicriterion View of the Performance Comparison Problem; 3.2 Illustrating with a Real Case; 4 Evaluating the Suite with Performance Profiles. 4.1 Illustrating with a Real Case5 Conclusions; References; Real-World Parameter Tuning Using Factorial Design with Parameter Decomposition; 1 Introduction; 2 Automated Tuning Framework; 3 Case Description; 4 s-step Decomposition Approach; 4.1 User-Guided Decomposition; 4.2 Automated Decomposition; 5 Experimental Results; 5.1 First Step; 5.1.1 Screening Phase; 5.1.2 Exploration Phase; 5.1.3 Exploitation Phase; 5.2 Second Step; 5.2.1 Screening Phase; 5.2.2 Exploration Phase; 5.2.3 Exploitation Phase; 6 Conclusion; References; Evolving Pacing Strategies for Team Pursuit Track Cycling. 1 Introduction2 Team Pursuit Track Cycling; 3 Problem Formulation; 3.1 Team Pursuit as an Optimisation Problem; 3.1.1 Task 1: Modified Model for Team Pursuit; 3.1.2 Task 2: Forward Integration Modelling for Race Time Computation; 4 Metaheuristic Approach; 4.1 Power Profile Optimisation; 4.2 Transition Strategy Optimisation; 5 Experiments; 5.1 Experimental Parameters; 5.2 Power Profile Optimisation; 5.3 Strategy Optimisation; 5.4 Statistical Significance of Results; 6 Conclusions and Future Work; References. A Dual Mutation Operator to Solve the Multi-objective Production Planning of Perishable Goods1 Introduction; 2 Literature Review; 3 Problem Definition; 3.1 Mathematical Model; 4 Multi-objective Hybrid Genetic Algorithm; 4.1 Representation of an Individual; 4.2 Genetic Operators; 4.2.1 Crossover and Standard Mutation; 4.2.2 Dual Mutation; 4.2.3 Selection/Reproduction; 4.3 Fitness of an Individual; 4.4 Initializing a Population; 4.5 Infeasible Individuals; 5 Computational Experiments; 5.1 Data Generation; 5.2 Evaluation Metrics; 5.3 Parameter Tuning; 5.4 Experimental Results. 6 Conclusions and Future ResearchReferences; Brain Cine-MRI Registration Using MLSDO Dynamic Optimization Algorithm; 1 Introduction; 2 The Registration Method; 2.1 The Matching Step; 2.2 The Registration Process; 3 The MLSDO Algorithm; 3.1 Description of the Algorithm; 3.2 Cine-MRI Registration as a Dynamic Optimization Problem; 3.3 Parameter Fitting of MLSDO; 4 Experimental Results and Discussion; 5 Conclusion; References; GRASP with Path Relinkingfor the Two-Echelon Vehicle Routing Problem; 1 Introduction; 2 Problem Definition and Literature Review; 3 GRASP with Path Relinking. 3.1 GRASP with Path Relinking for the 2E-VRP. Finite First Hitting Time versus Stochastic convergence in Particle Swarm Optimisation -- Using Performance Profiles for the Analysis and Design of Benchmark Experiments -- Real-World Parameter Tuning using Factorial Design with Parameter Decomposition -- Evolving Pacing Strategies for Team Pursuit Track Cycling -- A Dual Mutation Operator to Solve the Multi-objective Production Planning of Perishable Goods -- Brain cine-MRI Registration using MLSDO Dynamic Optimization Algorithm -- GRASP with Path Relinking for the Two-Echelon Vehicle Routing Problem -- A Hybrid (1+1)-Evolutionary Strategy for the Open Vehicle Routing Problem -- A Timeslot-Filling Heuristic Approach to Construct High-School Timetables -- A GRASP for Supply Chain Optimization with Financial Constraints per Production Unit.
Titelhinweis Buchausg. u.d.T.: ‡Advances in metaheuristics
ISBN ISBN 978-1-4614-6322-1
Klassifikation KJMD
KJT
BUS049000
*90-06
90C59
68T05
90C60
90B90
00B25
658.40301
519.6
HD30.23
Kurzbeschreibung Finite First Hitting Time versus Stochastic convergence in Particle Swarm Optimisation -- Using Performance Profiles for the Analysis and Design of Benchmark Experiments -- Real-World Parameter Tuning using Factorial Design with Parameter Decomposition -- Evolving Pacing Strategies for Team Pursuit Track Cycling -- A Dual Mutation Operator to Solve the Multi-objective Production Planning of Perishable Goods -- Brain cine-MRI Registration using MLSDO Dynamic Optimization Algorithm -- GRASP with Path Relinking for the Two-Echelon Vehicle Routing Problem -- A Hybrid (1+1)-Evolutionary Strategy for the Open Vehicle Routing Problem -- A Timeslot-Filling Heuristic Approach to Construct High-School Timetables -- A GRASP for Supply Chain Optimization with Financial Constraints per Production Unit
2. Kurzbeschreibung Metaheuristics have been a very active research topic for more than two decades. During this time many new metaheuristic strategies have been devised, they have been experimentally tested and improved on challenging benchmark problems, and they have proven to be important tools for tackling optimization tasks in a large number of practical applications. In other words, metaheuristics are nowadays established as one of the main search paradigms for tackling computationally hard problems. Still, there are a large number of research challenges in the area of metaheuristics. These challenges range from more fundamental questions on theoretical properties and performance guarantees, empirical algorithm analysis, the effective configuration of metaheuristic algorithms, approaches to combine metaheuristics with other algorithmic techniques, towards extending the available techniques to tackle ever more challenging problems. This edited volume grew out of the contributions presented at the ninth Metaheuristics International Conference that was held in Udine, Italy, 25-28 July 2011. The conference comprised 117 presentations of peer-reviewed contributions and 3 invited talks, and it has been attended by 169 delegates. The chapters that are collected in this book exemplify contributions to several of the research directions outlined above
SWB-Titel-Idn 379696436
Signatur Springer E-Book
Bemerkungen Elektronischer Volltext - Campuslizenz
Elektronische Adresse $uhttp://dx.doi.org/10.1007/978-1-4614-6322-1
Internetseite / Link Volltext
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