In recent years, metaheuristics have become important tools for solving hard optimization problems encountered in industry, as well as in the theoretical field. Many different metaheuristics exist, and new ones are under constant development. Some of the most famous examples are evolutionary algorithms (EAs), Tabu search (TS), simulated annealing (SA), ant colony optimization (ACO), particle swarm optimization (PSO), greedy randomized adaptive search procedures (GRASP), and memetic algorithms. This journal brings together outstanding research and recent developments in the field of metaheuristics and their applications in the industrial world. Among the subjects to be considered are theoretical developments in metaheuristics such as EAs, TS, SA, ACO, PSO, GRASP, memetic algorithms, variable neighbourhood search, guided local search, scatter search, and path relinking; performance comparisons of metaheuristics; cooperative methods combining different types of approaches such as constraint programming and mathematical programming techniques; parallel and distributed metaheuristics for multiobjective optimization; adaptation of discrete metaheuristics to continuous optimization; dynamic optimization; software implementations; and real-life applications.