Parallel metaheuristic is a class of techniques that are capable of reducing both the numerical effort and the run time of a
metaheuristic. To this end, concepts and technologies from the field of parallelism in
computer science are used to enhance and even completely modify the behavior of existing metaheuristics. Just as it exists a long list of metaheuristics like
evolutionary algorithms,
particle swarm,
ant colony optimization,
simulated annealing, etc. it also exists a large set of different techniques strongly or loosely based in these ones, whose behavior encompasses the multiple parallel execution of algorithm components that cooperate in some way to solve a problem on a given parallel hardware platform.