A Comparison of Multiobjective Depth-First Algorithms

Javier Coego, Lawrence Mandow, and J.L. Perez de la Cruz.
A comparison of multiobjective depth-first algorithms.
Journal of Intelligent Manufacturing, 1 March 2012:1-9, 2012.


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.


  journal={Journal of Intelligent Manufacturing},
  keywords={Branch and bound},
  url={http: //},
  author={Coego, Javier and Mandow, Lawrence and Perez de la Cruz, J.L.},
  title={A Comparison of Multiobjective Depth-First Algorithms},
  volume={1 March 2012},
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