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Documents authored by Li, Hongbo

Document
DOI: 10.4230/LIPIcs.CP.2026.59
Lightweight Look-Ahead-Based Value Heuristics for Constraint Optimization Problems

Authors: Ziyang Yu and Hongbo Li

Published in: LIPIcs, Volume 379, 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)

Abstract
The Bound-Impact Value Selector (BIVS) employs a look-ahead strategy to enable black-box Constraint Optimization Problem (COP) solvers to find high-quality solutions earlier. However, its computational cost prohibits its use throughout the entire search process. To mitigate this cost, the Restricted Fixpoint (RF) approach considers only the constraints on the shortest paths between the selected variable and the objective, yielding better performance. In this paper, we propose a lightweight strategy from a different perspective, named Assess Before Look-Ahead (ABLA), to enhance the performance of look-ahead-based value heuristics for solving COPs. ABLA first assesses whether the look-ahead process can differentiate between the values of a variable, and only performs the look-ahead when this assessment passes. Experiments on benchmark instances from recent MiniZinc Challenges demonstrate that ABLA’s decisions to skip redundant look-ahead processes are highly reliable, with an average accuracy of over 94%. Consequently, ABLA significantly boosts the performance of both BIVS and RF, outperforming two other baselines: the minimum value heuristic and the RLARF value heuristic.
Cite as

Ziyang Yu and Hongbo Li. Lightweight Look-Ahead-Based Value Heuristics for Constraint Optimization Problems. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 59:1-59:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{yu_et_al:LIPIcs.CP.2026.59,
  author =	{Yu, Ziyang and Li, Hongbo},
  title =	{{Lightweight Look-Ahead-Based Value Heuristics for Constraint Optimization Problems}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{59:1--59:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-432-1},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{379},
  editor =	{Beldiceanu, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2026.59},
  URN =		{urn:nbn:de:0030-drops-266921},
  doi =		{10.4230/LIPIcs.CP.2026.59},
  annote =	{Keywords: Constraint Optimization Problem, Value Heuristic, Look-Ahead}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.32
A Portfolio-Based Approach to Select Efficient Variable Ordering Heuristics for Constraint Satisfaction Problems

Authors: Hongbo Li, Yaling Wu, Minghao Yin, and Zhanshan Li

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Variable ordering heuristics (VOH) play a central role in solving Constraint Satisfaction Problems (CSP). The performance of different VOHs may vary greatly in solving the same CSP instance. In this paper, we propose an approach to select efficient VOHs for solving different CSP instances. The approach contains two phases. The first phase is a probing procedure that runs a simple portfolio strategy containing several different VOHs. The portfolio tries to use each of the candidate VOHs to guide backtracking search to solve the CSP instance within a limited number of failures. If the CSP is not solved by the portfolio, one of the candidates is selected for it by analysing the information attached in the search trees generated by the candidates. The second phase uses the selected VOH to guide backtracking search to solve the CSP. The experiments are run with the MiniZinc benchmark suite and four different VOHs which are considered as the state of the art are involved. The results show that the proposed approach finds the best VOH for more than 67% instances and it solves more instances than all the candidate VOHs and an adaptive VOH based on Multi-Armed Bandit. It could be an effective adaptive search strategy for black-box CSP solvers.
Cite as

Hongbo Li, Yaling Wu, Minghao Yin, and Zhanshan Li. A Portfolio-Based Approach to Select Efficient Variable Ordering Heuristics for Constraint Satisfaction Problems. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 32:1-32:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{li_et_al:LIPIcs.CP.2022.32,
  author =	{Li, Hongbo and Wu, Yaling and Yin, Minghao and Li, Zhanshan},
  title =	{{A Portfolio-Based Approach to Select Efficient Variable Ordering Heuristics for Constraint Satisfaction Problems}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{32:1--32:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.32},
  URN =		{urn:nbn:de:0030-drops-166616},
  doi =		{10.4230/LIPIcs.CP.2022.32},
  annote =	{Keywords: Constraint Satisfaction Problem, Variable Ordering Heuristic, Adaptive Search Heuristic, Portfolio}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.CP.2021.9
Failure Based Variable Ordering Heuristics for Solving CSPs (Short Paper)

Authors: Hongbo Li, Minghao Yin, and Zhanshan Li

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
Variable ordering heuristics play a central role in solving constraint satisfaction problems. In this paper, we propose failure based variable ordering heuristics. Following the fail first principle, the new heuristics use two aspects of failure information collected during search. The failure rate heuristics consider the failure proportion after the propagations of assignments of variables and the failure length heuristics consider the length of failures, which is the number of fixed variables composing a failure. We performed a vast experiments in 41 problems with 1876 MiniZinc instances. The results show that the failure based heuristics outperform the existing ones including activity-based search, conflict history search, the refined weighted degree and correlation-based search. They can be new candidates of general purpose variable ordering heuristics for black-box CSP solvers.
Cite as

Hongbo Li, Minghao Yin, and Zhanshan Li. Failure Based Variable Ordering Heuristics for Solving CSPs (Short Paper). In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 9:1-9:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{li_et_al:LIPIcs.CP.2021.9,
  author =	{Li, Hongbo and Yin, Minghao and Li, Zhanshan},
  title =	{{Failure Based Variable Ordering Heuristics for Solving CSPs}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{9:1--9:10},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.9},
  URN =		{urn:nbn:de:0030-drops-153002},
  doi =		{10.4230/LIPIcs.CP.2021.9},
  annote =	{Keywords: Constraint Satisfaction Problem, Variable Ordering Heuristic, Failure Rate, Failure Length}
}
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