LIPIcs.CP.2023.24.pdf
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MDD Archive for Boosting the Pareto Constraint
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29th International Conference on Principles and Practice of Constraint Programming (CP 2023)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Principles and Practice of Constraint Programming (CP) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-09-22
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Steve Malalel, Arnaud Malapert, Marie Pelleau, and Jean-Charles Régin. MDD Archive for Boosting the Pareto Constraint. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 24:1-24:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.CP.2023.24
https://doi.org/10.4230/LIPIcs.CP.2023.24
Abstract
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.
Subject Classification
ACM Subject Classification
- Applied computing → Multi-criterion optimization and decision-making
- Theory of computation → Constraint and logic programming
- Mathematics of computing → Decision diagrams
Keywords
- Constraint Programming
- Global Constraint
- MDD
- Multi-Objective Problem
- Pareto Constraint
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References
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