A unified view of metaheuristics
This book provides a complete background on metaheuristics and
shows readers how to design and implement efficient algorithms to
solve complex optimization problems across a diverse range of
applications, from networking and bioinformatics to engineering
design, routing, and scheduling. It presents the main design
questions for all families of metaheuristics and clearly
illustrates how to implement the algorithms under a software
framework to reuse both the design and code.
Throughout the book, the key search components of metaheuristics
are considered as a toolbox for:
* Designing efficient metaheuristics (e.g. local search, tabu
search, simulated annealing, evolutionary algorithms, particle
swarm optimization, scatter search, ant colonies, bee colonies,
artificial immune systems) for optimization problems
* Designing efficient metaheuristics for multi-objective
optimization problems
* Designing hybrid, parallel, and distributed metaheuristics
* Implementing metaheuristics on sequential and parallel
machines
Using many case studies and treating design and implementation
independently, this book gives readers the skills necessary to
solve large-scale optimization problems quickly and efficiently. It
is a valuable reference for practicing engineers and researchers
from diverse areas dealing with optimization or machine learning;
and graduate students in computer science, operations research,
control, engineering, business and management, and applied
mathematics.
Inhoudsopgave
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 Paradis EO.
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 Paradis EO.
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 Paradis EO.
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 Paradis EO.
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 Paradis EO.
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.
Over de auteur
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.
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