From Design to Implementation
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.
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.