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黑翼风与 Osprey 融合的启发式优化算法及其工程应用

Zheng Zhang¹, Xiangkun Wang², Yinggao Yue²

¹School of Information Engineering, Wenzhou Business College, Wenzhou 325035, China.

Biomimetics (Basel, Switzerland)

|October 25, 2024

概括

这项研究介绍了Osprey融合的混沌黑翼算法 (OCBKA),以改善群群智能优化. 增强的OCBKA有效地平衡全球和本地搜索,在复杂的工程问题中超越现有方法.

科学领域:

计算智能是一种计算智能.
群集情报群集情报群集情报
优化算法优化算法

背景情况:

多目标优化问题由于其高维的目标空间而带来挑战.
现有的群集智能方法,如黑翼算法 (BKA),在平衡全球勘探和当地开发方面扎,往往导致过早的融合.
提高群集智能算法的优化能力对于解决复杂的现实问题至关重要.

研究的目的:

为了提出一个增强的群集智能优化算法,Osprey融合的混沌黑翼算法 (OCBKA).
改进黑翼算法 (BKA) 的全球搜索和本地开发能力.
解决现有算法在处理高维多目标优化问题的局限性.

主要方法:

使用物流混乱映射对人口进行初始化,以获得更好的多样性.
将Osprey优化算法与黑翼算法 (BKA) 融合在一起,以提高搜索性能.
使用CEC2005和CEC2021基准函数和三个工程优化问题的性能评估.

主要成果:

拟议的OCBKA在平衡全球搜索和当地发展方面取得了重大改进.
数字比较显示了OCBKA在基准函数上的优越性能与其他群集智能方法相比.
该算法有效地解决复杂的工程优化问题,具有高精度和快速融合.

结论:

与 Osprey 融合的混沌黑翼风算法 (OCBKA) 是一种具有竞争力和有效的优化策略.
OCBKA提供了增强的搜索功能,解决了原始BKA的局限性.
该算法显示了在复杂的工程优化任务中应用的强大潜力,这些任务需要高的融合精度和速度.

关键词:

考希突变是一种突变.黑翼风算法黑翼风算法工程应用问题工程应用问题这种算法是Metaheuristic算法.奥斯普雷优化算法的优化算法

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