An ant colony algorithm Hybridized with Iterated Local Search for the QAP

The quadratic assignment problem (QAP) is considered one of the hardest combinatorial optimization problems. 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 (ACAILS) with iterated local search (ILS), is proposed. In ACAILS, only partial facilities are randomly chosen to compute the designed probability. Experimental results demonstrate that the proposed approach can obtain the better quality of the solutions.