ABSTRACT
A metaheuristic is a general algorithmic framework for finding solutions to optimization problems. Within this framework underlying local heuristics are guided and adapted to effectively explore the solution space. Metaheuristics are designed to be robust. That is, solutions to a wide variety of optimization problems can be found with relatively few modifications. Examples of metaheuristics include simulated annealing (SA), tabu search (TS), iterated local search (ILS), evolutionary algorithms (EA), evolutionary programs (EP), greedy randomized adaptive search procedure (GRASP), memetic algorithms (MA), variable neighborhood descent (VND), genetic algorithms (GA), scatter search (SS) and ant colony optimization (ACO). In addition, there are many hybrid approaches combining features of several of these techniques simultaneously.
