贝伯行为优化器:用于太阳能光伏参数识别和工程问题的新型元启发算法
Kaichen OuYang1, Dedai Wei2, Xinye Sha3
1Department of Mathematics, University of Science and Technology of China, Hefei 230026, China.
Journal of advanced research
|September 6, 2025
概括
一个新的Beaver行为优化器 (BBO) 在复杂的工程和太阳能光伏 (PV) 系统优化方面表现出色. 这种生物启发的算法有效地找到最佳解决方案,
科学领域:
- 计算智能
- 群集智能算法
- 生物启发的计算
背景情况:
- 对于太阳能光伏 (PV) 系统和工程而言,数值优化至关重要,但传统方法面临复杂,高维度问题的挑战.
- 现有的优化技术在非线性环境中难以有效地找到解决方案, 阻碍了太阳能等领域的发展.
研究的目的:
- 介绍新型的海行为优化器 (BBO),这是由海水建设行为所启发的群体智能算法.
- 验证BBO在基准测试功能和现实工程问题上的有效性,重点是太阳能光伏参数优化.
主要方法:
- 基于海行为建模的BBO,包含不同的探索和开发阶段.
- 在2017年CEC和2022年CEC的基准函数上测试了BBO的各种维度 (10到100).
- 将BBO应用于三个太阳能光伏参数识别问题和四个工程设计问题,与其他11个算法进行比较.
主要成果:
- 在所有基准功能上,BBO表现优异,在太阳能光伏和工程优化任务中排名第一.
- 在大多数场景中,该算法表现优于最先进的方法,表现出强大的趋同性和最小的结果差异.
- 统计测试证实了BBO的业绩改善的重要性.
结论:
- 贝弗行为优化器 (BBO) 已被证实是复杂优化的强大工具,特别是在太阳能光伏和工程设计中.
- BBO的生物启发方法有效地平衡了勘探和开发,提供了竞争优势.
- 这项研究强调了BBO在高要求的优化挑战中提供高效准确的解决方案的潜力.
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