Consultant-Guided Search Algorithms for the Quadratic Assignment Problem

被引：0
|
作者：
Iordache, Serban [1 ]
机构：
[1] SCOOP Software GmbH, D-51105 Cologne, Germany
来源：
GECCO-2010 COMPANION PUBLICATION: PROCEEDINGS OF THE 12TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2010年
关键词：
Metaheuristics; combinatorial optimization; swarm intelligence; quadratic assignment problem;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Consultant-Guided Search (CGS) is a recent swarm intelligence metaheuristic for combinatorial optimization problems, inspired by the way real people make decisions based on advice received from consultants. Until now, CGS has been successfully applied to the Traveling Salesman Problem. Because a good metaheuristic should be able to tackle efficiently a large variety of problems, it is important to see how CGS behaves when applied to other classes of problems. In this paper, we propose four CGS algorithms for the Quadratic Assignment Problem (QAP) and we compare their performance. Our experimental results show that CGS is able to compete with Ant Colony Optimization in terms of solution quality.
引用
收藏
页码：2089 / 2090
页数：2
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