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一个多战略改进的饥饿游戏搜索算法
Qiu Yihui1, Zhang Xinqiang2, Li Ruoyu1
1College of Economics and Management, Xiamen University of Technology, Xiamen, 361000, China.
Scientific reports
|August 22, 2025
概括
多策略改进的饥饿游戏搜索 (MHGS) 算法通过平衡探索和利用来提高优化. 这种新的方法提高了复杂的计算智能问题的准确性和多样性.
科学领域:
- 计算智能
- 优化算法
- 超听觉搜索
背景情况:
- 原始的饥饿游戏搜索 (HGS) 受到了探索/开发的不平衡,人口多样性低,以及过早的融合.
- 解决这些局限性对于提高元启发优化算法的性能至关重要.
研究的目的:
- 引入一个多策略改进的饥饿游戏搜索算法 (MHGS).
- 克服原始HGS算法的固有缺点.
主要方法:
- 实施阶段性位置更新框架,以动态协调全球勘探和当地开采.
- 结合了由生物模式启发的增强繁殖操作员,以保持种群多样性.
- 开发了一种自适应边界处理机制,以及具有自我调整系数的精英动态对立学习策略.
主要成果:
- 在7个最先进的算法中,MHGS在基准函数和工程问题上的精度平均提高了23.7%.
- 一个二进制变体BMHGS_V3在UCI数据集上获得了92.3%的平均分类准确性.
- 统计分析 (Wilcoxon等级和试验,p < 0. 05) 证实了MHGS显著的性能提升.
结论:
- 提出的MHGS算法为解决复杂的优化问题提供了一个新且有效的框架.
- MHGS在计算智能方面具有显著的理论和实践价值.
- 其组件的协同整合提高了搜索的稳定性和效率.
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