关于混合算法的研究,该算法结合了增强的群优化和双倍改进的模拟化通过集群在旅行销售员问题 (TSP) 中
Tan Hao1, Wu Yingnian1, Zhang Jiaxing1
1School of Automation, Beijing Information Science and Technology University, Beijing, China.
PeerJ. Computer science
|October 9, 2023
概括
这项研究通过结合殖民地优化和模拟化与集群来增强旅行销售员问题 (TSP) 解决方案. 改进的算法为复杂的物流挑战提供了更快的融合和更好的解决方案质量.
科学领域:
- 计算智能是一种计算智能.
- 运营研究 运营研究
- 组合优化的优化.
背景情况:
- 旅行销售员问题 (TSP) 是一个复杂的优化挑战.
- 现有的算法,如殖民地优化 (ACO) 和模拟化 (SA) 有局限性,如停滞,缓慢的收和局部最佳.
- 数据集的变化影响了ACO和SA的业绩.
研究的目的:
- 解决 ACO 和 SA 在解决 TSP. 的局限性.
- 提高TSP的融合速度和解决方案质量.
- 开发一个混合算法,结合ACO,SA和集群,以提高TSP性能.
主要方法:
- 开发了一种新的混合算法,集成殖民地优化,模拟化和集群方法.
- 拟议的算法在各种旅行销售员问题库 (TSPLIB) 数据集上进行了测试.
- 绩效是根据融合速度和解决方案质量来评估的.
主要成果:
- 与传统的ACO和SA相比,混合算法显示了显著改善的融合速度.
- 在各种 TSPLIB 数据集中观察到解决方案质量的提高.
- 提出的方法在几个基准指标中表现优于其他现有的改进算法.
结论:
- 结合集群,ACO和SA方法有效地克服了TSP单个算法的局限性.
- 这种增强的算法为解决复杂的优化问题提供了卓越的方法.
- 在物流运输中的成功应用凸显了其实际实用性和有效性.
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