The variable ordering heuristic is an important module in algorithms dedicated to solve Constraint Satisfaction Problems (CSP), while it impacts the efficiency of exploring the search space and the size of the search tree. It also exploits, often implicitly, the structure of the instances. In this paper, we propose Conflict-History Search (CHS), a dynamic and adaptive variable ordering heuristic for CSP solving. It is based on the search failures and considers the temporality of these failures throughout the solving steps. The exponential recency weighted average is used to estimate the evolution of the hardness of constraints throughout the search. The experimental evaluation on XCSP3 instances shows that integrating CHS to solvers based on MAC (Maintaining Arc Consistency) and BTD (Backtracking with Tree Decomposition) achieves competitive results and improvements compared to the state-of-the-art heuristics. Beyond the decision problem, we show empirically that the solving of the constraint optimization problem (COP) can also take advantage of this heuristic.

变量排序启发式是专用于解决约束满足问题（CSP）的算法中的重要模块，同时它影响探索搜索空间的效率和搜索树的大小。它还通常隐式地利用实例的结构。在本文中，我们提出了冲突历史搜索（CHS），这是一种用于 CSP 求解的动态自适应变量排序启发式算法。它基于搜索失败，并在整个求解步骤中考虑这些失败的暂时性。指数新近度加权平均值用于估计整个搜索过程中约束硬度的演变。对 XCSP3 实例的实验评估表明，与最先进的启发式方法相比，将 CHS 集成到基于 MAC（维护弧一致性）和 BTD（树分解回溯）的求解器可实现有竞争力的结果和改进。除了决策问题之外，我们还凭经验证明约束优化问题（COP）的求解也可以利用这种启发式方法。