新基准+多解算法，让经济分析更真实可靠
Linear Ordering Problem: Time for a Change
- 用最新经济数据构建全新评测基准
- 可生成多个高质量且差异大的最优解
- 适合关注经济结构分析的决策者与研究者
线性排序问题（LOP）是组合优化中的基础问题，在经济学、社会选择和机器学习中有重要应用，尤其用于经济投入产出表的三角化，以识别关键产业。现有算法多基于过时的宏观经济数据评估，无法反映当代经济结构。此外，LOP实例常存在多个显著不同的全局最优解，单一解难以满足实际需求。为此，本文提出基于最新真实经济数据的新基准套件，并设计一种利用前沿元启发式算法生成多样高质量解的方案，同时提供评估解质量与多样性的度量标准。实验在新基准上对比了传统单解设置与新增的多解场景下的表现。
原文摘要 · Abstract (English)
The Linear Ordering Problem (LOP) is a fundamental combinatorial optimization problem with important applications in areas such as economics, social choice, and machine learning. Its most prominent use is the triangulation of economic input-output tables, which helps identify critical industries in an economy. Most existing algorithms have been evaluated on benchmarks derived from outdated macroeconomic data, which no longer reflect the structure of contemporary economies. Furthermore, LOP instances often exhibit many distinct global optima that can differ substantially from one another, creating challenges for applications that rely on a single solution. To address these limitations, we introduce a novel benchmark suite derived from up-to-date real-world economic data and an algorithmic scheme that leverages state-of-the-art LOP metaheuristics to generate diverse sets of high-quality solutions, together with metrics for assessing both quality and diversity. Experiments were conducted to report results on the proposed benchmark suite under both the traditional single-solution setting and the newly introduced multi-solution scenario
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