English(EN) Decomposability-Guided Cooperative Coevolution for Large-Scale Itinerary Planning

新算法应对大规模行程规划挑战

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了一种新的多目标协同共进化算法，旨在解决大规模行程规划问题。该方法分析组件间的依赖关系，以建立弱可分解性的条件，从而实现将集群作为连续子路径访问的最优路径。该算法包含动态分解策略，考虑基于组件规模和贡献的优化潜力，并采用计算资源分配策略。在34个真实世界和基准测试用例上的评估表明，所提出的算法实现了最高的平均超体积，并且显著优于现有方法，尤其是在大规模实例上。 AI

影响这项研究可能为物流和旅行规划应用中的更高效路线优化带来可能。

排序理由详细介绍行程规划新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Ziyu Zhang, Peilan Xu, Yuetong Sun, Yuhui Shi, Wenjian Luo · 2026-10-15 04:00

面向大规模行程规划的分解引导协同共进化

arXiv:2506.06121v3 Announce Type: replace Abstract: Large-scale itinerary planning is a variant of the traveling salesman problem, aiming to determine an optimal path that maximizes the collected point-of-interest (POI) scores while minimizing travel time and cost, subject to tra…

本文如何被排名

报道来源 [1]

面向大规模行程规划的分解引导协同共进化

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