In this paper, we propose new depth-first heuristic search algorithms to approximate the set of Pareto optimal solutions in multi-objective constraint optimization. Our approach builds upon recent advances in multi-objective heuristic search over weighted AND/OR search spaces and uses an ε-dominance relation between cost vectors to significantly reduce the set of non-dominated solutions. Our empirical evaluation on various benchmarks demonstrates the power of our scheme which improves the resolution times dramatically over recent state-of-the-art competitive approaches. © 2011 Springer-Verlag.
CITATION STYLE
Marinescu, R. (2011). Efficient approximation algorithms for multi-objective constraint optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6992 LNAI, pp. 150–164). https://doi.org/10.1007/978-3-642-24873-3_12
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