一个改进的河马优化算法,基于自适应性开发和解决方案多样性增强
Shengyu Pei1,2, Gang Sun3, Lang Tong4
1School of Artiffcal Intelligence, Guangxi Minzu University, Nanning, Guangxi, China.
PeerJ. Computer science
|June 26, 2025
概括
这项研究通过混乱初始化和自适应策略来增强河马优化算法. 改进的算法在解决复杂的,高维度的优化问题方面表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超听证学是一种超听证学.
背景情况:
- 传统的河马优化算法面临着复杂问题的融合和解决方案多样性的局限性.
- 高维和多式功能对现有的优化技术构成重大挑战.
研究的目的:
- 提出一个改进的河马优化算法,解决融合和解决方案多样性的局限性.
- 在复杂的优化任务中增强全球搜索能力,避免局部优化.
主要方法:
- 集成的混乱地图初始化,以优化初始人口分布.
- 引入了适应性开发机制,以平衡勘探和开发阶段.
- 实施了使用非线性扰动的解决方案多样性增强策略.
主要成果:
- 与原始算法相比,改进的算法显示出明显更好的融合速度和解决方案准确性.
- 在标准基准函数 (CEC17,CEC22) 上表现优于其他主流优化算法.
- 统计分析证实了优越的勘探-开发平衡,特别是在高维多方式功能方面.
结论:
- 增强的河马优化算法为复杂的,高维度的问题提供了更好的性能.
- 该研究为推进元启发式优化技术提供了有效的策略.
- 拟议的改进为未来的计算智能研究提供了有价值的见解.
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