This paper addresses distributed task allocation in complex scenarios modeled using the distributed constraint optimization problem (DCOP) formalism. We propose and evaluate a novel algorithm for distributed task allocation based on theoretical models of division of labor in social insect colonies, called Swarm-GAP. Swarm-GAP was experimented in an abstract centralized simulation environment and in the RoboCup Rescue Simulator. We show that Swarm-GAP achieves similar results to other recent proposed algorithm with a dramatic reduction in communication and computation. Thus, our approach is highly scalable regarding both the number of agents and tasks.