On the design of ACO for the biobjective quadratic assignment problem

Few applications of ACO algorithms to multiobjective problems have been presented so far and it is not clear how to design an effective ACO algorithms for such problems. In this article, we study the performance of several ACO variants for the biobjective Quadratic Assignment Problem that are based on two fundamentally different search strategies. The first strategy is based on dominance criteria, while the second one exploits different scalarizations of the objective function vector. Further variants differ in the use of multiple colonies, the use of local search, and the pheromone update strategy. The experimental results indicate that the use of local search procedures and the correlation between objectives play an essential role in the performance of the variants studied in this paper.