发表机构
University of Lübeck; TU Bergakademie Freiberg（吕贝克大学; 弗莱贝格矿业技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对实时应用在严格时间限制下的离散优化问题，提出元启发式优化框架STILO，集成ACO、GA和SA的细粒度配置空间，实验表明其离散距离机制和问题无关图结构在不同时间限制下有效性各异，算法效果受问题类型、实例特征和计算预算影响。
AI 中文摘要
实时应用通常依赖于能够在毫秒量级内为困难问题找到高质量解决方案的优化方法。元启发式优化框架（MOF）是此类任务的有用工具，因为它们提供了大量通用的搜索机制，可以在不同的计算预算下返回解决方案。然而，现有工作在很大程度上忽略了可用计算时间作为分析的一个明确维度。在这项工作中，我们引入了STILO，一个专门为严格时间限制下的优化而设计的MOF。STILO集成了蚁群优化（ACO）、遗传算法（GA）和模拟退火（SA）的细粒度配置空间，结合了现有和新的算子。我们使用各种离散优化问题的合成和基准实例进行了实验。结果表明，所提出的用于SA的离散距离计算机制在严格时间限制下的优化中是有用的。它们还表明，所提出的与问题无关的ACO图结构的相对有效性可能随时间限制而变化，即使对于相同的实例特征也是如此。更一般地，结果表明算法族和算子的有效性不仅取决于问题类型，还取决于实例的特征和可用的计算预算。
英文摘要
Real-time applications often rely on optimization approaches that can find high-quality solutions to hard problems on the order of milliseconds. Metaheuristic optimization frameworks (MOFs) are useful tools for such tasks, as they provide large sets of general-purpose search mechanisms that can return solutions under different computational budgets. However, existing work largely overlooks the available computation time as an explicit dimension of analysis. In this work, we introduce STILO, a MOF specifically designed for optimization under strict time limits. STILO integrates fine-grained configuration spaces for ant colony optimization (ACO), genetic algorithm (GA), and simulated annealing (SA), combining existing and novel operators. We performed experiments using both synthetic and benchmark instances of various discrete optimization problems. The results indicate that the proposed discrete distance calculation mechanism for SA is useful for optimization under strict time limits. They also show that the relative effectiveness of the proposed problem-independent graph structures for ACO can vary across time limits, even for the same instance characteristics. More generally, the results demonstrate that the effectiveness of algorithm families and operators depends not only on the problem type, but also on the characteristics of the instance and the available computational budget.
