Conference
MDD Archive for Boosting the Pareto Constraint
| Title: | MDD Archive for Boosting the Pareto Constraint |
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| Authors: | Malalel, Steve, Malapert, Arnaud, Pelleau, Marie, Régin, Jean-Charles |
| Contributors: | Université Côte d'Azur (UCA), This work has been supported by the French government, through the 3IA Côte d’Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002., Yap, Roland H. C., ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019) |
| Source: | 29th International Conference on Principles and Practice of Constraint Programming (CP 2023) https://hal.science/hal-04241596 29th International Conference on Principles and Practice of Constraint Programming (CP 2023), Aug 2023, Toronto, Canada. pp.24:1--24:15, ⟨10.4230/LIPIcs.CP.2023.24⟩ |
| Publisher Information: | HAL CCSD Schloss Dagstuhl - Leibniz-Zentrum für Informatik |
| Publication Year: | 2023 |
| Collection: | Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
| Subject Terms: | 2012 ACM Subject Classification Applied computing → Multi-criterion optimization and decisionmaking Theory of computation → Constraint and logic programming Mathematics of computing → Decision diagrams phrases Constraint Programming, Global Constraint, MDD, Multi-Objective Problem, Pareto Constraint, 2012 ACM Subject Classification Applied computing → Multi-criterion optimization and decisionmaking, Theory of computation → Constraint and logic programming, Mathematics of computing → Decision diagrams phrases Constraint Programming, Constraint Programming, Applied computing → Multi-criterion optimization and decision-making, Mathematics of computing → Decision diagrams, [INFO]Computer Science [cs] |
| Subject Geographic: | Toronto |
| Time: | Toronto, Canada |
| Description: | International audience ; Multi-objective problems are frequent in the real world. In general they involve several incomparable objectives and the goal is to find a set of Pareto optimal solutions, i.e. solutions that are incomparable two by two. In order to better deal with these problems in CP the global constraint Pareto was developed by Schaus and Hartert to handle the relations between the objective variables and the current set of Pareto optimal solutions, called the archive. This constraint handles three operations: adding a new solution to the archive, removing solutions from the archive that are dominated by a new solution, and reducing the bounds of the objective variables. The complexity of these operations depends on the size of the archive. In this paper, we propose to use a multi-valued Decision Diagram (MDD) to represent the archive of Pareto optimal solutions. MDDs are a compressed representation of solution sets, which allows us to obtain a compressed and therefore smaller archive. We introduce several algorithms to implement the above operations on compressed archives with a complexity depending on the size of the archive. We show experimentally on bin packing and multi-knapsack problems the validity of our approach. |
| Document Type: | conference object |
| Language: | English |
| Relation: | hal-04241596; https://hal.science/hal-04241596; https://hal.science/hal-04241596/document; https://hal.science/hal-04241596/file/LIPIcs-CP-2023-24.pdf |
| DOI: | 10.4230/LIPIcs.CP.2023.24 |
| Availability: | https://hal.science/hal-04241596 https://hal.science/hal-04241596/document https://hal.science/hal-04241596/file/LIPIcs-CP-2023-24.pdf https://doi.org/10.4230/LIPIcs.CP.2023.24 |
| Rights: | info:eu-repo/semantics/OpenAccess |
| Accession Number: | edsbas.BC931E5E |
| Database: | BASE |
| DOI: | 10.4230/LIPIcs.CP.2023.24 |
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