Enables the readers to design and deploy powerful algorithms (methodologies and implementations) to solve complex optimization problems in a diverse range of industries. Includes numerous real-world examples to demonstrate how metaheuristics are applied Shows how to generate an efficient program from the design using the software framework.

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.

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.