Multi-objectivising the Quadratic Assignment Problem by Means of an Elementary Landscape Decomposition

被引:3
作者
Ceberio, Josu [1 ]
Calvo, Borja [1 ]
Mendiburu, Alexander [1 ]
Lozano, Jose A. [1 ]
机构
[1] Univ Basque Country UPV EHU, Fac Comp Sci, Intelligent Syst Grp, Donostia San Sebastian 20018, Gipuzkoa, Spain
来源
ADVANCES IN ARTIFICIAL INTELLIGENCE (CAEPIA 2015) | 2015年 / 9422卷
关键词
Multi-objectivisation; Quadratic assignment problem; Elementary landscape decomposition; Interchange neighbourhood; LOCAL SEARCH; ALGORITHM;
D O I
10.1007/978-3-319-24598-0_26
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms in this field could be used for solving single-objective problems. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to apply multi-objectivisation schemes in their optimisation. In this paper, we follow this idea by presenting a method to multi-objectivise single-objective problems based on an elementary landscape decomposition of their objective function. In order to illustrate this procedure, we consider the elementary landscape decomposition of the Quadratic Assignment Problem under the interchange neighbourhood. In particular, we propose reformulating the QAP as a multi-objective problem, where each elementary landscape in the decomposition is an independent function to be optimised. In order to validate this multi-objectivisation scheme, we implement a version of NSGA-II for solving the multi-objective QAP, and compare its performance with that of a GA on the single-objective QAP. Conducted experiments show that the multi-objective approach is better than the single-objective approach for some types of instances.
引用
收藏
页码:289 / 300
页数:12
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