InhaltsangabePreface. Acknowledgments. Glossary. 1 Common Concepts for Metaheuristics. 1.1 Optimization Models. 1.2 Other Models for Optimization. 1.3 Optimization Methods. 1.4 Main Common Concepts for Metaheuristics. 1.5 Constraint Handling. 1.6 Parameter Tuning. 1.7 Performance Analysis of Metaheuristics. 1.8 Software Frameworks for Metaheuristics. 1.9 Conclusions. 1.10 Exercises. 2 SingleSolution Based Metaheuristics. 2.1 Common Concepts for Single-Solution Based Metaheuristics. 2.2 Fitness Landscape Analysis. 2.3 Local Search. 2.4 Simulated Annealing. 2.5 Tabu Search. 2.6 Iterated Local Search. 2.7 Variable Neighborhood Search. 2.8 Guided Local Search. 2.9 Other Single-Solution Based Metaheuristics. 2.10 SMetaheuristic Implementation Under ParadisEO. 2.11 Conclusions. 2.12 Exercises. 3 PopulationBased Metaheuristics. 3.1 Common Concepts for Population-Based Metaheuristics. 3.2 Evolutionary Algorithms. 3.3 Common Concepts for Evolutionary Algorithms. 3.4 Other Evolutionary Algorithms. 3.5 Scatter Search. 3.6 Swarm Intelligence. 3.7 Other Population-Based Methods. 3.8 Pmetaheuristics Implementation Under ParadisEO. 3.9 Conclusions. 3.10 Exercises. 4 Metaheuristics for Multiobjective Optimization. 4.1 Multiobjective Optimization Concepts. 4.2 Multiobjective Optimization Problems. 4.3 Main Design Issues of Multiobjective Metaheuristics. 4.4 Fitness Assignment Strategies. 4.5 Diversity Preservation. 4.6 Elitism. 4.7 Performance Evaluation and Pareto Front Structure. 4.8 Multiobjective Metaheuristics Under ParadisEO. 4.9 Conclusions and Perspectives. 4.10 Exercises. 5 Hybrid Metaheuristics. 5.1 Hybrid Metaheuristics. 5.2 Combining Metaheuristics with Mathematical Programming. 5.3 Combining Metaheuristics with Constraint Programming. 5.4 Hybrid Metaheuristics with Machine Learning and Data Mining. 5.5 Hybrid Metaheuristics for Multiobjective Optimization. 5.6 Hybrid Metaheuristics Under ParadisEO. 5.7 Conclusions and Perspectives. 5.8 Exercises. 6 Parallel Metaheuristics. 6.1 Parallel Design of Metaheuristics. 6.2 Parallel Implementation of Metaheuristics. 6.3 Parallel Metaheuristics for Multiobjective Optimization. 6.4 Parallel Metaheuristics Under ParadisEO. 6.5 Conclusions and Perspectives. 6.6 Exercises. Appendix: UML and C++. A.1 A Brief Overview of UML Notations. A.2 A Brief Overview of the C++ Template Concept. References. Index.

El-ghazali Talbi is a full Professor in Computer Science at the University of Lille (France), and head of the optimization group of the Computer Science Laboratory (L.I.F.L.). His current research interests are in the fields of metaheuristics, parallel algorithms, multi-objective combinatorial optimization, cluster and grid computing, hybrid and cooperative optimization, and application to bioinformatics, networking, transportation, and logistics. He is the founder of the conference META (International Conference on Metaheuristics and Nature Inspired Computing), and is head of the INRIA Dolphin project dealing with robust multi-objective optimization of complex systems.

Leseprobe

Preface. Acknowledgments. Glossary. 1 Common Concepts for Metaheuristics. 1.1 Optimization Models. 1.2 Other Models for Optimization. 1.3 Optimization Methods. 1.4 Main Common Concepts for Metaheuristics. 1.5 Constraint Handling. 1.6 Parameter Tuning. 1.7 Performance Analysis of Metaheuristics. 1.8 Software Frameworks for Metaheuristics. 1.9 Conclusions. 1.10 Exercises. 2 Single-Solution Based Metaheuristics. 2.1 Common Concepts for Single-Solution Based Metaheuristics. 2.2 Fitness Landscape Analysis. 2.3 Local Search. 2.4 Simulated Annealing. 2.5 Tabu Search. 2.6 Iterated Local Search. 2.7 Variable Neighborhood Search. 2.8 Guided Local Search. 2.9 Other Single-Solution Based Metaheuristics. 2.10 S-Metaheuristic Implementation Under ParadisEO. 2.11 Conclusions. 2.12 Exercises. 3 Population-Based Metaheuristics. 3.1 Common Concepts for Population-Based Metaheuristics. 3.2 Evolutionary Algorithms. 3.3 Common Concepts for Evolutionary Algorithms. 3.4 Other Evolutionary Algorithms. 3.5 Scatter Search. 3.6 Swarm Intelligence. 3.7 Other Population-Based Methods. 3.8 P-metaheuristics Implementation Under ParadisEO. 3.9 Conclusions. 3.10 Exercises. 4 Metaheuristics for Multiobjective Optimization. 4.1 Multiobjective Optimization Concepts. 4.2 Multiobjective Optimization Problems. 4.3 Main Design Issues of Multiobjective Metaheuristics. 4.4 Fitness Assignment Strategies. 4.5 Diversity Preservation. 4.6 Elitism. 4.7 Performance Evaluation and Pareto Front Structure. 4.8 Multiobjective Metaheuristics Under ParadisEO. 4.9 Conclusions and Perspectives. 4.10 Exercises. 5 Hybrid Metaheuristics. 5.1 Hybrid Metaheuristics. 5.2 Combining Metaheuristics with Mathematical Programming. 5.3 Combining Metaheuristics with Constraint Programming. 5.4 Hybrid Metaheuristics with Machine Learning and Data Mining. 5.5 Hybrid Metaheuristics for Multiobjective Optimization. 5.6 Hybrid Metaheuristics Under ParadisEO. 5.7 Conclusions and Perspectives. 5.8 Exercises. 6 Parallel Metaheuristics. 6.1 Parallel Design of Metaheuristics. 6.2 Parallel Implementation of Metaheuristics. 6.3 Parallel Metaheuristics for Multiobjective Optimization. 6.4 Parallel Metaheuristics Under ParadisEO. 6.5 Conclusions and Perspectives. 6.6 Exercises. Appendix: UML and C++. A.1 A Brief Overview of UML Notations. A.2 A Brief Overview of the C++ Template Concept. References. Index.