Combinatorial optimization and in particular the great variety of fascinating problemsthatbelong to thisareahaveattractedmanyresearchersformorethan halfacentury.Duetothepracticalrelevanceofsolvinghardreal-worldproblems, much research e?ort has been devoted to the development of heuristic methods aimed at ?nding good approximate solutions in a reasonable computation time. Some solution paradigms that are not speci?c for one particular problem have been deeply studied in the past, and the term metaheuristic is now common for such optimization heuristics. Several metaheuristics – simulated annealing, - netic and evolutionary algorithms, tabu search, ant colony optimization, scatter search, iterated local search, and greedy randomized adaptive search procedures beingsomeofthem–havefoundtheirownresearchcommunities,andspecialized conferences devoted to such techniques have been organized. Plenty of classical hard problems, such as the quadratic assignment pr- lem, the traveling salesman problem, problems in vehicle routing, scheduling, and timetabling, etc., have been tackled successfully with metaheuristic - proaches. Several thereof are currently considered state-of-the-art methods for solving such problems. However, for many years the main focus of research was on the application of single metaheuristics to given problems. A tendency to compare di?erent metaheuristics against each other could be observed, and sometimes this competition led to thinking in stereotypes in the research communities.