A hybrid Memory-based ACO algorithm for the QAP

The performance of ant colony optimization (ACO) algorithms significantly improves when hybridized with local search procedures which strongly bias the search towards promising regions of the search space. In this work, we study a recently proposed Memory based ACO algorithm (MACO) which incorporates some tabu search principles into the solution construction process. This algorithm has also been hybridized with two local search procedures: 2-opt (M-ACO-2opt) and Tabu Search (M-ACO-TS). The performances of the two hybrid versions of M-ACO are analyzed on a set of instances of the Quadratic Assignment Problem (QAP). The results show that the hybrid versions of M-ACO are able to improve the quality of the best known solutions for several of the instances studied.