Autore	Talbi El-Ghazali <1965->
Pubbl/distr/stampa	Hoboken, NJ, : John Wiley & Sons, 2009
Descrizione fisica	1 online resource (625 p.)
Disciplina	519.6
Collana	Wiley Series on Parallel and Distributed Computing
Soggetto topico	Mathematical optimization Heuristic programming Problem solving - Data processing Computer algorithms
ISBN	1-282-18844-5 9786612188442 0-470-49691-6 0-470-49690-8
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	METAHEURISTICS; CONTENTS; Preface; Acknowledgments; Glossary; 1 Common Concepts for Metaheuristics; 1.1 Optimization Models; 1.1.1 Classical Optimization Models; 1.1.2 Complexity Theory; 1.1.2.1 Complexity of Algorithms; 1.1.2.2 Complexity of Problems; 1.2 Other Models for Optimization; 1.2.1 Optimization Under Uncertainty; 1.2.2 Dynamic Optimization; 1.2.2.1 Multiperiodic Optimization; 1.2.3 Robust Optimization; 1.3 Optimization Methods; 1.3.1 Exact Methods; 1.3.2 Approximate Algorithms; 1.3.2.1 Approximation Algorithms; 1.3.3 Metaheuristics; 1.3.4 Greedy Algorithms 1.3.5 When Using Metaheuristics?1.4 Main Common Concepts for Metaheuristics; 1.4.1 Representation; 1.4.1.1 Linear Representations; 1.4.1.2 Nonlinear Representations; 1.4.1.3 Representation-Solution Mapping; 1.4.1.4 Direct Versus Indirect Encodings; 1.4.2 Objective Function; 1.4.2.1 Self-Sufficient Objective Functions; 1.4.2.2 Guiding Objective Functions; 1.4.2.3 Representation Decoding; 1.4.2.4 Interactive Optimization; 1.4.2.5 Relative and Competitive Objective Functions; 1.4.2.6 Meta-Modeling; 1.5 Constraint Handling; 1.5.1 Reject Strategies; 1.5.2 Penalizing Strategies 1.5.3 Repairing Strategies1.5.4 Decoding Strategies; 1.5.5 Preserving Strategies; 1.6 Parameter Tuning; 1.6.1 Off-Line Parameter Initialization; 1.6.2 Online Parameter Initialization; 1.7 Performance Analysis of Metaheuristics; 1.7.1 Experimental Design; 1.7.2 Measurement; 1.7.2.1 Quality of Solutions; 1.7.2.2 Computational Effort; 1.7.2.3 Robustness; 1.7.2.4 Statistical Analysis; 1.7.2.5 Ordinal Data Analysis; 1.7.3 Reporting; 1.8 Software Frameworks for Metaheuristics; 1.8.1 Why a Software Framework for Metaheuristics?; 1.8.2 Main Characteristics of Software Frameworks 1.8.3 ParadisEO Framework1.8.3.1 ParadisEO Architecture; 1.9 Conclusions; 1.10 Exercises; 2 Single-Solution Based Metaheuristics; 2.1 Common Concepts for Single-Solution Based Metaheuristics; 2.1.1 Neighborhood; 2.1.2 Very Large Neighborhoods; 2.1.2.1 Heuristic Search in Large Neighborhoods; 2.1.2.2 Exact Search in Large Neighborhoods; 2.1.2.3 Polynomial-Specific Neighborhoods; 2.1.3 Initial Solution; 2.1.4 Incremental Evaluation of the Neighborhood; 2.2 Fitness Landscape Analysis; 2.2.1 Distances in the Search Space; 2.2.2 Landscape Properties; 2.2.2.1 Distribution Measures 2.2.2.2 Correlation Measures2.2.3 Breaking Plateaus in a Flat Landscape; 2.3 Local Search; 2.3.1 Selection of the Neighbor; 2.3.2 Escaping from Local Optima; 2.4 Simulated Annealing; 2.4.1 Move Acceptance; 2.4.2 Cooling Schedule; 2.4.2.1 Initial Temperature; 2.4.2.2 Equilibrium State; 2.4.2.3 Cooling; 2.4.2.4 Stopping Condition; 2.4.3 Other Similar Methods; 2.4.3.1 Threshold Accepting; 2.4.3.2 Record-to-Record Travel; 2.4.3.3 Great Deluge Algorithm; 2.4.3.4 Demon Algorithms; 2.5 Tabu Search; 2.5.1 Short-Term Memory; 2.5.2 Medium-Term Memory; 2.5.3 Long-Term Memory; 2.6 Iterated Local Search 2.6.1 Perturbation Method
Record Nr.	UNINA-9910143080603321