向公平和身临其境的户外定位:人工智能驱动的多目标路线规划框架与增强的沙猫群集优化
Qingzhu Lun1, Boya Li1, Yuehui Zhou1
1School of Sports Science, Qufu Normal University, Qufu, Shandong, China.
PloS one
|March 11, 2026
概括
本研究介绍了五个设计原则和一个新的计算框架,使用增强的沙猫群优化 (SCSO) 算法来创建更公平,更有吸引力的户外定位路线.
科学领域:
- 娱乐地理 娱乐地理 娱乐地理
- 计算优化计算优化
- 运动科学 运动科学 运动科学
背景情况:
- 户外定位运动是一个日益增长的全球运动,需要复杂的路线设计.
- 平衡竞争公平性和参与者体验是规划人员面临的关键挑战.
- 现有的方法往往不能有效地优化定位路线.
研究的目的:
- 建立公平和引人入胜的定位路线的基本设计原则.
- 开发一个新的计算框架,以优化定向路线设计.
- 增强户外娱乐活动中的用户体验.
主要方法:
- 制定路线设计作为一个受约束的多目标优化问题.
- 开发了一个增强的沙猫群优化 (SCSO) 算法用于解决方案生成.
- 通过对50个不同的地形形状进行模拟来验证框架.
主要成果:
- 该SCSO算法证明了有效的解决方案生成路线优化.
- 在路线最佳度指标中观察到持续的性能改进.
- 新的框架显著超过了传统的路线设计方法.
结论:
- 拟议的设计原则和计算框架有效地平衡了竞争公平和用户体验.
- 这项研究为娱乐路线规划提供了理论见解和实际工具.
- 增强的SCSO算法为户外运动中的复杂优化问题提供了一种高效的方法.
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