The rst edition of the Handbook of Metaheuristics was published in 2003 under the editorship of Fred Glover and Gary A. Kochenberger. Given the numerous - velopments observed in the eld of metaheuristics in recent years, it appeared that the time was ripe for a second edition of the Handbook. For different reasons, Fred and Gary were unable to accept Springers invitation to prepare this second e- tion and they suggested that we should take over the editorship responsibility of the Handbook. We are deeply honored and grateful for their trust. As stated in the rst edition, metaheuristics are "solution methods that orch- trate an interaction between local improvement procedures and higher level stra- gies to create a process capable of escaping from local optima and performing a robust search of a solution space. " Although this broad characterization still holds today, many new and exciting developments and extensions have been observed in the last few years. We think in particular to hybrids, which take advantage of the strengths of each of their individual metaheuristic components to better explore the solution space.
Hybrids of metaheuristics with other optimization techniques, like branch-and-bound, mathematical programming or constraint programming are also increasingly popular. On the front of applications, metaheuristics are now used to nd high-quality solutions to an ever-growing number of complex, ill-de ned re- world problems, in particular combinatorial ones.
Simulated Annealing.- Tabu Search.- Variable Neighborhood Search.- Scatter Search and Path-Relinking: Fundamentals, Advances, and Applications.- Genetic Algorithms.- A Modern Introduction to Memetic Algorithms.- Genetic Programming.- Ant Colony Optimization: Overview and Recent Advances.- Advanced Multi-start Methods.- Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications.- Guided Local Search.- Iterated Local Search: Framework and Applications.- Large Neighborhood Search.- Artificial Immune Systems.- A Classification of Hyper-heuristic Approaches.- Metaheuristic Hybrids.- Parallel Meta-heuristics.- Reactive Search Optimization: Learning While Optimizing.- Stochastic Search in Metaheuristics.- An Introduction to Fitness Landscape Analysis and Cost Models for Local Search.- Comparison of Metaheuristics.