文献类型：会议论文

英文题名：Hybrid Ant Colony Algorithm for QAP

作者：Qi, Chengming[2];Tian, Wenjie[2];Sun, Yunchuan[1]

第一作者：亓呈明

通讯作者：Sun, YC[1]

机构：[1]Beijing Normal Univ, Sch Econ & Business Adm, Beijing 100875, Peoples R China;[2]Beijing Union Univ, Coll Automat, Beijing 100101, Peoples R China

第一机构：Beijing Normal Univ, Sch Econ & Business Adm, Beijing 100875, Peoples R China

通讯机构：[1]corresponding author), Beijing Normal Univ, Sch Econ & Business Adm, Beijing 100875, Peoples R China.

会议论文集：2nd ISECS International Colloquium on Computing, Communication, Control and Management (CCCM 2009)

会议日期：AUG 08-09, 2009

会议地点：Sanya, PEOPLES R CHINA

语种：英文

外文关键词：Ant colony algorithm; Combinatorial optimization; Pareto local search; QAP

摘要：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.