适应式差异化优化:使用风能预测应用进行全球优化的多策略增强算法
Guanjun Lin1, Mahmoud Abdel-Salam2, Gang Hu3
1School of Information Engineering, Sanming University, Sanming 365004, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
自适应差异化优化算法 (ADPO) 通过提高种群多样性和搜索效率来增强原来的优化算法 (PO). 在复杂的优化任务和风能预测中,ADPO表现出卓越的性能.
科学领域:
- 计算智能
- 超听觉优化
- 灵感来自自然的算法
背景情况:
- 优化算法 (PO) 是一种基于行为的自然启发的元启发方法.
- 在复杂的优化问题中,PO面临着人口多样性和早期融合的挑战.
- 现有的算法难以保持搜索效率并确定最佳解决方案.
研究的目的:
- 引入适应差异化优化算法 (ADPO),以克服PO的局限性.
- 增强优化算法的探索,利用和融合能力.
- 验证ADPO在基准功能和实际应用中的有效性.
主要方法:
- 开发了三种新机制的ADPO:平均差异变化 (MDV),基于维度学习的狩猎 (DLH) 和增强的适应性互惠主义 (EAM).
- MDV采用双相突变来实现平衡的勘探和开采.
- DLH使用维度智能学习来防止过早的融合,并保持多样性.
- 为了实现平衡的强化和多样化,EAM引入了以健身为导向的互动.
主要成果:
- 在CEC2017和CEC2022基准函数上,ADPO表现出卓越的融合速度,搜索效率和解决方案精度.
- 在使用LSTM进行风能预测时,ADPO的平均R2值为0.9726,超过了传统方法.
- 在12个先进的算法中,ADPO始终取得优越的弗里德曼排名 (1.42-2.78).
结论:
- 与基线PO相比,拟议的ADPO显著提高了优化能力.
- 在复杂的优化和可再生能源预测方面,ADPO表现出强的性能和有效性.
- 这些新机制有效地解决了人口多样性和融合问题.
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