A hybrid Memory-based ACO algorithm for the QAP

Author

Leguizamón, Guillermo ; Arito, Franco ; Coello, Carlos A Coello

Author_Institution

LIDIC, Univ. Nac. de San Luis, San Luis, Argentina

fYear

2010

fDate

18-23 July 2010

Firstpage

1

Lastpage

8

Abstract

The performance of ant colony optimization (ACO) algorithms significantly improves when hybridized with local search procedures which strongly bias the search towards promising regions of the search space. In this work, we study a recently proposed Memory based ACO algorithm (MACO) which incorporates some tabu search principles into the solution construction process. This algorithm has also been hybridized with two local search procedures: 2-opt (M-ACO-2opt) and Tabu Search (M-ACO-TS). The performances of the two hybrid versions of M-ACO are analyzed on a set of instances of the Quadratic Assignment Problem (QAP). The results show that the hybrid versions of M-ACO are able to improve the quality of the best known solutions for several of the instances studied.

Keywords

quadratic programming; search problems; QAP; ant colony optimization; hybrid memory based ACO algorithm; local search procedure; quadratic assignment problem; search space; tabu search; Algorithm design and analysis; Benchmark testing; Construction industry; Equations; Optimization; Probabilistic logic; Time frequency analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2010 IEEE Congress on

Conference_Location

Barcelona

Print_ISBN

978-1-4244-6909-3

Type

conf

DOI

10.1109/CEC.2010.5586132

Filename

5586132