Abstract
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.
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References
Dechter, R.: Constraint Processing. Morgan Kaufmann Publishers, San Francisco (2003)
Junker, U.: Preference-based inconsistency proving: when the failure of the best is sufficient. In: European Conference on Artificial Intelligence (ECAI), pp. 118–122 (2006)
Rollon, E., Larrosa, J.: Bucket elimination for multi-objective optimization problems. Journal of Heuristics 12, 307–328 (2006)
Rollon, E., Larrosa, J.: Multi-objective propagation in constraint programming. In: European Conference on Artificial Intelligence (ECAI), pp. 128–132 (2006)
Marinescu, R.: Exploiting problem decomposition in multi-objective constraint optimization. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 592–607. Springer, Heidelberg (2009)
Hansen, P.: Bicriterion path problems. In: Multicriteria Decision Making (1980)
Warburton, A.: Approximation of Pareto optima in multiple-objective shortest problems. Operations Research 35(1), 70–79 (1987)
Tsaggouris, G., Zaroliagis, C.: Multiobjective optimization: improved fptas for shortest paths and non-linear objectives with applications. Theory of Comp. Sys. 45(1), 162–186 (2009)
Papadimitriou, C., Yannakakis, M.: On the approximability of trade-offs and optimal access to web sources. In: FOCS, pp. 86–92 (2000)
Erlebach, T., Kellerer, H., Pferschy, U.: Approximating multiobjective knapsack problems. Management Sciences 48(12), 1603–1612 (2002)
Bazgan, C., Hugot, H., Vanderpooten, D.: A practical efficient fptas for the 0-1 multi-objective knapsack problem. In: Arge, L., Hoffmann, M., Welzl, E. (eds.) ESA 2007. LNCS, vol. 4698, pp. 717–728. Springer, Heidelberg (2007)
Dubus, J.-P., Gonzales, C., Perny, P.: Multiobjective optimization using GAI models. In: International Conference on Artificial Intelligence (IJCAI), pp. 1902–1907 (2009)
Perny, P., Spanjaard, O.: Near admissible algorithms for multiobjective search. In: European Conference on Artificial Intelligence (ECAI), pp. 490–494 (2008)
Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multiobjective optimization. Evolutionary Computation 3(10), 263–282 (2002)
Dechter, R., Mateescu, R.: AND/OR search spaces for graphical models. Artificial Intelligence 171(2-3), 73–106 (2007)
Freuder, E.C., Quinn, M.J.: Taking advantage of stable sets of variables in constraint satisfaction problems. In: IJCAI, pp. 1076–1078 (1985)
Rollon, E., Larrosa, J.: Multi-objective Russian doll search. In: AAAI Conference on Artificial Intelligence, pp. 249–254 (2007)
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Marinescu, R. (2011). Efficient Approximation Algorithms for Multi-objective Constraint Optimization. In: Brafman, R.I., Roberts, F.S., Tsoukiàs, A. (eds) Algorithmic Decision Theory. ADT 2011. Lecture Notes in Computer Science(), vol 6992. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24873-3_12
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DOI: https://doi.org/10.1007/978-3-642-24873-3_12
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