Hybrid ant colony algorithm for QAP

Ant colony algorithm (ACA), inspired by the food-searching behavior of ants, is an evolutionary algorithm and performs well in discrete optimization. In this paper, through an analysis of the constructive procedure of the solution in the ACA, a hybrid ant colony system (PLSACA) with Pareto local search (PLS), is proposed. In PLSACA, only partial facilities are randomly chosen to compute the designed probability and some of the solutions are intensified using local search. Computational results demonstrate that our algorithm generates competitive results compared to the best known solutions of the test instances.