Applying an extended guided local search to the quadratic assignment problem

被引：29
|
作者：
Mills, P [1 ]
Tsang, E [1 ]
Ford, J [1 ]
机构：
[1] Univ Essex, Dept Comp Sci, Colchester CO4 3SQ, Essex, England
来源：
ANNALS OF OPERATIONS RESEARCH | 2003年 / 118卷 / 1-4期
关键词：
local search; meta-heuristics; quadratic assignment problem;
D O I：
10.1023/A:1021857607524
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
In this paper, we show how an extended Guided Local Search (GLS) can be applied to the Quadratic Assignment Problem (QAP). GLS is a general, penalty-based meta-heuristic, which sits on top of local search algorithms, to help guide them out of local minima. We present empirical results of applying several extended versions of GLS to the QAP, and show that these extensions can improve the range of parameter settings within which Guided Local Search performs well. Finally, we compare the results of running our extended GLS with some state of the art algorithms for the QAP.
引用
收藏
页码：121 / 135
页数：15
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