Ant Colonies optimization take inspiration from the behavior of real ant co
lonies to solve optimization problems. This paper presents a parallel model
for ant colonies to solve the quadratic assignment problem (QAP). The coop
eration between simulated ants is provided by a pheromone matrix that plays
the role of a global memory. The exploration of the search space is guided
by the evolution of pheromones levels, while exploitation has been boosted
by a tabu local search heuristic. Special care has also been taken in the
design of a diversification phase, based on a frequency matrix. We give res
ults that have been obtained on benchmarks from the QAP library. We show th
at they compare favorably with other algorithms dedicated for the QAP. (C)
2001 Elsevier Science B.V. All rights reserved.