一个基于等级链的阿基米德优化算法
Zijiao Zhang1, Chong Wu1, Shiyou Qu1
1School of Management, Harbin Institute of Technology, Harbin 150000, China.
Mathematical biosciences and engineering : MBE
|December 21, 2023
概括
基于等级链的阿基米德优化算法 (HCAOA) 提高了收,并通过对待不同的代理来避免局部最佳. 这种增强的算法在基准测试套件和工程问题上表现出卓越的性能.
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:
- 阿基米德的优化算法 (AOA) 是一种以其效率和最小参数而闻名的元启发式算法.
- 佳能AOA由于均的剂量处理而遭受缓慢的收和对局部最佳的敏感性.
研究的目的:
- 为了解决标准阿基米德优化算法 (AOA) 的局限性.
- 提出一个改进的基于等级链的AOA (HCAOA),以提高优化性能.
主要方法:
- 开发了HCAOA,这是一种新的算法,根据代理人的性能水平对代理人进行分层.
- 实现了对最佳个体进行折射对立的直角学习机制.
- 基于飞行的阿基米德螺旋机制,用于高级个人,并为普通个人应用常规的AOA.
- 整合了一种多策略边界处理机制,以促进人口多样性.
主要成果:
- 与标准的AOA和其他先进的竞争对手算法相比,HCAOA在CEC 2017测试套件中表现出更高的性能.
- 拟议的HCAOA在四个复杂的工程设计问题上取得了竞争优化结果.
- 实验结果验证了HCAOA在克服局部最佳值和加速融合方面的有效性.
结论:
- HCAOA有效地增强了阿基米德优化算法的探索和利用能力.
- 层次方法和多策略边界处理显著提高了优化速度和人口多样性.
- 对于理论优化任务和实际工程挑战,HCAOA提供了一个强大而有效的解决方案.
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