一个连续的人工蜂群算法,用于解决无能力设施的位置问题
Meiqing An1, Wanli Xiang2, Yuxing Jiang1
1School of Traffic & Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, Gansu, People's Republic of China.
Scientific reports
|February 12, 2026
概括
一个新的连续人工蜂群 (ABC) 算法,cABC,有效地解决了离散优化问题,如无容量设施位置问题 (UFLP). 与现有方法相比,这种增强的群集智能方法提高了解决方案的准确性和稳定性.
科学领域:
- 优化算法 优化算法
- 群集情报 群集情报 群集情报
- 运营研究 运营研究
背景情况:
- 人工蜂群 (ABC) 算法是一种流行的蜂群智能方法,用于持续优化.
- 标准ABC在没有复杂的修改的情况下与离散优化问题和无容量设施位置问题 (UFLP) 斗争.
- 现有的智能算法通常需要提高UFLP的解决方案质量和偏差.
研究的目的:
- 提出一个连续ABC (cABC) 算法,专门设计用于解决离散优化问题的传统ABC的局限性,特别是UFLP.
- 通过整合新的初始化,解决方案转换和搜索强化机制来提高ABC的性能.
- 为了验证拟议的cABC算法的有效性和优越性,与基准数据集的既定方法相比.
主要方法:
- 开发了一种连续ABC (cABC) 算法,能够使用混乱初始化在连续空间中演变.
- 实现了一个概率分离机制,以将连续解决方案转换为UFLP的二进制向量.
- 引入了不可行解决方案的动态修复策略,随机指导机制和时间变化的扰动方案,以改进搜索.
- 整合了修改后的概率选择机制和基于对立的学习来增强勘探和开发.
主要成果:
- 在CAP数据集上,cABC最初与传统的ABC算法进行比较时显示出有效性.
- 进一步的验证表明cABC在CAP和M*数据集上表现优于其他11种已知的方法.
- 实验结果表明,cABC的解决方案精度和稳定性优于最先进的方法.
结论:
- 拟议的连续ABC (cABC) 算法为离散优化问题提供了强大而准确的解决方案,特别是UFLP.
- 在处理离散优化任务时,cABC 成功克服了传统 ABC 的局限性.
- 集成到cABC中的新机制显著提高了其搜索性能和解决方案质量,使其成为最先进的方法.
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