A hybrid genetic algorithmic approach to the maximally diverse grouping problem

被引：28

作者：

Fan, Z. P. ^{[2
]}

Chen, Y. ^{[1
]}

Ma, J. ^{[3
]}

Zeng, S. ^{[4
]}

机构：

[1] Shanghai Univ Finance & Econ, Dept Informat Management & Engn, Shanghai 200433, Peoples R China

[2] Northeastern Univ, Shenyang, Peoples R China

[3] City Univ Hong Kong, Kowloon, Hong Kong, Peoples R China

[4] Univ Arizona, Tucson, AZ 85721 USA

来源：

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY | 2011年 / 62卷 / 01期

关键词：

genetic algorithm; maximally diverse grouping problem; local neighbourhood search; APPROXIMATION ALGORITHMS; ASSIGNING STUDENTS;

D O I：

10.1057/jors.2009.168

中图分类号：

C93 [管理学];

学科分类号：

12 ; 1201 ; 1202 ; 120202 ;

摘要：

The maximally diverse grouping problem (MDGP) is a NP-complete problem. For such NP-complete problems, heuristics play a major role in searching for solutions. Most of the heuristics for MDGP focus on the equal group-size situation. In this paper, we develop a genetic algorithm (GA)-based hybrid heuristic to solve this problem considering not only the equal group-size situation but also the different group-size situation. The performance of the algorithm is compared with the established Lotfi-Cerveny-Weitz algorithm and the non-hybrid GA. Computational experience indicates that the proposed GA-based hybrid algorithm is a good tool for solving MDGP. Moreover, it can be easily modified to solve other equivalent problems. Journal of the Operational Research Society (2011) 62, 92-99. doi:10.1057/jors.2009.168 Published online 6 January 2010

引用

页码：92 / 99

页数：8

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