Title :
An ant colony algorithm Hybridized with Iterated Local Search for the QAP
Author_Institution :
Beijing Union Univ., Beijing, China
Abstract :
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.
Keywords :
combinatorial mathematics; evolutionary computation; quadratic programming; search problems; QAP; combinatorial optimization problem; discrete optimization; evolutionary algorithm; hybrid ant colony algorithm; iterated local search; quadratic assignment problem; Ant colony optimization; Approximation algorithms; Computational intelligence; Computer industry; Evolutionary computation; System testing; Traveling salesman problems; Ant colony system; Iterated local search; Quadratic assignment problem;
Conference_Titel :
Computational Intelligence and Industrial Applications, 2009. PACIIA 2009. Asia-Pacific Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4606-3
DOI :
10.1109/PACIIA.2009.5406542