启发式多目标离散优化中的受限决策图
Heuristic Multiobjective Discrete Optimization using Restricted Decision Diagrams
AI总结：
本文提出基于受限决策图的启发式方法，通过不同规则、机器学习和深度学习选择节点，有效近似Pareto前沿，实验显示在多目标背包、集合包装和旅行商问题中恢复超过85%的前沿并实现2.5倍加速。
摘要：
决策图（DDs）已成为精确多目标整数线性规划的最新方法。当DD过大无法存储或决策者偏好快速近似Pareto前沿时，必须限制DD的子集（状态或节点）。本文引入新的节点选择启发式方法，通过简单规则、特征工程的机器学习或端到端深度学习构建受限DD，产生高质量Pareto前沿近似。实验表明，在多目标背包、集合包装和旅行商问题中，该方法恢复超过85%的Pareto前沿，相比精确DD枚举平均快2.5倍，非Pareto解极少。代码可在https://github.com/rahulptel/HMORDD获取。
英文摘要：
Decision diagrams (DDs) have emerged as a state-of-the-art method for exact multiobjective integer linear programming. When the DD is too large to fit into memory or the decision-maker prefers a fast approximation to the Pareto frontier, the complete DD must be restricted to a subset of its states (or nodes). We introduce new node-selection heuristics for constructing restricted DDs that produce a high-quality approximation of the Pareto frontier. Depending on the structure of the problem, our heuristics are based on either simple rules, machine learning with feature engineering, or end-to-end deep learning. Experiments on multiobjective knapsack, set packing, and traveling salesperson problems show that our approach is highly effective, recovering over 85% of the Pareto frontier while achieving 2.5x speedups over exact DD enumeration on average, with very few non-Pareto solutions. The code is available at https://github.com/rahulptel/HMORDD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
