Hybrid ant colony algorithm for QAP

Author

Qi, Chengming ; Tian, WenJie ; Sun, Yunchuan

Author_Institution

Coll. of Autom., Beijing Union Univ., Beijing, China

Volume

3

fYear

2009

fDate

8-9 Aug. 2009

Firstpage

213

Lastpage

216

Abstract

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.

Keywords

Pareto optimisation; combinatorial mathematics; evolutionary computation; probability; quadratic programming; search problems; Pareto local search; QAP; discrete combinatorial optimization; evolutionary algorithm; food-searching ant behavior; hybrid ant colony algorithm; probability; quadratic assignment problem; Ant colony optimization; Automatic control; Automation; Business communication; Communication system control; Costs; Evolutionary computation; Sun; Testing; Traveling salesman problems; Ant colony algorithm; Combinatorial optimization; Pareto local search; QAP;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on

Conference_Location

Sanya

Print_ISBN

978-1-4244-4247-8

Type

conf

DOI

10.1109/CCCM.2009.5267884

Filename

5267884