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An Architecture for Composite Combinatorial Optimization Solvers

Authors Khalil Chrit , Jean-François Baffier , Pedro Patinho , Salvador Abreu

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ACM Subject Classification
  • Software and its engineering → Constraint and logic languages
  • Software and its engineering → Formal language definitions
  • Theory of computation → Discrete optimization
Keywords
  • Hybrid Metaheuristics
  • DSL

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Abstract

In this paper, we introduce elements for MoSCO, a framework for building hybrid metaheuristic-based solvers from a collection of reusable base components. The framework is implemented in Julia and provides a modular architecture for composing solvers through a pipeline-based approach. The modular design of MoSCO supports the creation of reusable components and adaptable solver strategies for various Constraint Satisfaction Problems (CSPs) and Constraint Optimization Problems (COPs). We validate MoSCO’s utility through practical examples, demonstrating its effectiveness in reconstructing established metaheuristics and enabling the creation of novel solver configurations. This work lays the foundation for future developments in automated solver construction and parameter optimization.

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Khalil Chrit, Jean-François Baffier, Pedro Patinho, and Salvador Abreu. An Architecture for Composite Combinatorial Optimization Solvers. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Open Access Series in Informatics (OASIcs), Volume 135, pp. 8:1-8:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/OASIcs.SLATE.2025.8

Author Details

Khalil Chrit
  • NOVA LINCS, University of Évora, Portugal
Jean-François Baffier
  • IIJ Research, Tokyo, Japan
Pedro Patinho
  • NOVA LINCS, University of Évora, Portugal
Salvador Abreu
  • NOVA LINCS, University of Évora, Portugal

Funding

This work was partially supported by the Portuguese Foundation for Science and Technology under PhD fellowship UI/BD/153047/2022 and strategic project UID/04516 (NOVA LINCS.)

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