A comparison of multiobjective depth-first algorithms

被引：3
|
作者：
Coego, J. [1 ]
Mandow, L. [1 ]
Perez de la Cruz, J. L. [1 ]
机构：
[1] Univ Malaga, Dpto Lenguajes & Ciencias Comp, E-29071 Malaga, Spain
来源：
JOURNAL OF INTELLIGENT MANUFACTURING | 2013年 / 24卷 / 04期
关键词：
Multiobjective; Linear-space; Search; Iterative-deepening; Branch-and-bound; SHORTEST-PATH PROBLEMS; FRONTIER SEARCH;
D O I：
10.1007/s10845-012-0632-y
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Many real world problems involve several, usually conflicting, objectives. Multiobjective analysis deals with these problems locating trade-offs between different optimal solutions. Regarding graph search problems, several algorithms based on best-first and depth-first approaches have been proposed to return the set of all Pareto optimal solutions. This article presents a detailed comparison between two representatives of multiobjective depth-first algorithms, PIDMOA* and MO-DF-BnB. Both of them extend previous single-objective search algorithms with linear-space requirements to the multiobjective case. Experimental analyses on their time performance over tree-shaped search spaces are presented. The results clarify the fitness of both algorithms to parameters like the number or depth of goal nodes.
引用
收藏
页码：821 / 829
页数：9
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