A comparison of multiobjective depth-first algorithms

Authors:
J. Coego;L. Mandow;J. L. Pérez De La Cruz
Affiliations:
Dpto. Lenguajes y Ciencias de la Computación, Universidad de Málaga, Malaga, Spain 29071;Dpto. Lenguajes y Ciencias de la Computación, Universidad de Málaga, Malaga, Spain 29071;Dpto. Lenguajes y Ciencias de la Computación, Universidad de Málaga, Malaga, Spain 29071
Venue:
Journal of Intelligent Manufacturing
Year:
2013

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Abstract

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.