From Design to Implementation
1. Edition July 2009
2009. 624 Pages, Hardcover
ISBN 978-0-470-27858-1 - John Wiley & Sons
E-Books are also available on all known E-Book shops.
Metaheuristics provides a complete background of metaheuristics, enabling readers to design and deploy powerful algorithms to solve complex optimization problems in a diverse range of industries. Using case studies in different domains, including telecommunications, transportation and logistics, bioinformatics, design engineering, and scheduling provides clear information for these diverse markets. The book is an effective resource for engineers, researchers, and developers, and an ideal text for graduate students in computer science, bioinformatics, electrical engineering, and applied mathematics courses.
From the contents
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.
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.
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.
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.
4.7 Performance Evaluation and Pareto Front Structure.
4.8 Multiobjective Metaheuristics Under ParadisEO.
4.9 Conclusions and Perspectives.
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.
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.
Appendix: UML and C++.
A.1 A Brief Overview of UML Notations.
A.2 A Brief Overview of the C++ Template Concept.