一个增强的遗传算法解决方案,用于考虑各种约束的行程建议
Muhammed Şehab1,2, Metin Turan2
1Computer Technology Department, Avrupa Vocational School, Kocaeli Health and Technology University, Kocaeli, Turkey.
PeerJ. Computer science
|December 9, 2024
概括
本研究介绍了一种增强的遗传算法 (GA),用于优化旅游行程,考虑成本和时间等因素. 改进的GA为热门目的地提供了更有效,更准确的旅行建议.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 运营研究 运营研究
背景情况:
- 路线建议是一个复杂的优化问题.
- 现有的方法往往无法解释不同的旅游限制和偏好.
- 有效和个性化的旅行计划对旅游业至关重要.
研究的目的:
- 开发一个增强的遗传算法 (GA) 来优化旅游行程.
- 纳入诸如距离,时间,成本,预算和所需活动等多种约束.
- 提高行程推系统的效率和准确性.
主要方法:
- 开发了一个增强的遗传算法 (GA).
- 设计了一个定制的健身功能,以处理旅游业特定的复杂性.
- 实施了包括"复制顺序交叉"和修改的锦标赛选择在内的新方法.
- 交换突变方法得到了增强,以提高效率和适应性.
主要成果:
- 增强的GA实现了显著的改进率:伊斯坦布尔数据集的43.89%,缅甸数据集的56.60%.
- 提出的方法在时间,效率和准确性方面表现出优于替代方法的优势.
- 实验评估验证了对基准和定制数据集的增强GA的有效性.
结论:
- 增强的GA为旅游行程推问题提供了更好的解决方案.
- 该方法有效地平衡了多种约束和偏好,以实现最佳的行程生成.
- 这项研究为个性化旅行计划系统的未来进步提供了有希望的方向.
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