Improving the Ant Colony Optimization Algorithm for the Quadratic Assignment Problem

The quadratic assignment problem (QAP) is a well known important combinatorial problem. Indeed, many real world applications such as backboard wiring, typewriter keyboard design and scheduling can be formulated as QAPs. Recently, tackling this problem has been addressed by ant colony optimization (ACO) Algorithms. To do so, ACOs, and more precisely min-max ant system (MMAS) Algorithms, are usually combined with two kinds of stochastic local search (SLS) methods: the 2-opt local search and the tabu local search. We talk then respectively about MMAS_2opt and MMAS_tabu. In this paper, we propose an improvement of these two methods according to the properties of ACO and QAP. In the case of MMAS_2opt, a new random walk strategy is used to avoid a quick stagnation into local optima. Moreover, a forward-looking strategy is proposed to explore the neighborhood more thoroughly. In the case of MMAS_tabu, a random walk strategy is also employed to avoid getting stuck at local optima. In order to show the merits of our proposed techniques we have conducted experimental tests comparing respectively MMAS_2opt and MMAS_tabu with and without the improvements. The results demonstrate that the improved local method, have better performance in terms of the quality of the solution returned than the original ones. Moreover, we also noticed that the improved methods outperform each other for different classes of problems.