The field of metaheuristics has been fast evolving. Techniques such as simulated annealing, tabu search, genetic algorithms, scatter search, greedy randomized adaptive search, variable neighbourhood search, ant systems, and their hybrids are currently among the most efficient and robust optimization strategies to find high-quality solutions to many real-life optimization problems. A very large number of successful applications of metaheuristics are reported in the literature and spread throughout many books, journals, and conference proceedings. A series of international conferences entirely devoted to the theory, applications, and computational developments in metaheuristics has been attracting an increasing number of participants, from universities and the industry. For this text, specialists have written surveys on the following subjects: simulated annealing (E. Aarts and J. Korst, The Netherlands): noising methods (I. Charon and O. Hudry, France); strategies for the parallel implementation of metaheuristics (V.-D. Cung and C. Roucairol, France, and S.L. Martins and C.C. Ribeiro, Brazil); greedy randomized adaptive search procedures (P. Festa, Italy, and M.G.C. Resende, USA); tabu search (M. Gendreau, Canada); variable neighborhood search (P. Hansen and N. Mladenovic, Canada), ant colonies (V. Maniezzo and A. Carbonaro, Italy); and evolutionary algorithms (H. Mohlenbein and Th. Mahnig, Germany). Several further essays address issues or variants of metaheuristics, as well as innovative or successful applications of metaheuristics to classical or new combinatorial optimization problems.