一种混合黑翼风算法与PSO和差异突变,用于卓越的全球优化和工程应用
Xuemei Zhu1, Jinsi Zhang2, Chaochuan Jia3
1Experimental Training Teaching Management Department, West Anhui University, Yu'an District, Lu'an 237012, China.
Biomimetics (Basel, Switzerland)
|April 25, 2025
概括
本研究介绍了BKAPI,这是一个增强的混合算法,将黑翼算法 (BKA) 与粒子群优化 (PSO) 和差异进化 (DE) 结合起来,有效地解决高维优化问题. 在复杂的场景中,BKAPI提高了趋同速度和准确性,克服了过早的趋同问题.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 启发式搜索 启发式搜索
背景情况:
- 黑翼算法 (BKA) 在高维优化中面临着过早的融合.
- 现有的算法经常在平衡探索和开发方面扎.
- 参数灵敏度和局部搜索不足限制了BKA的有效性.
研究的目的:
- 提出一个增强的混合算法 (BKAPI) 来解决BKA的局限性.
- 为了提高高维优化的融合速度和计算精度.
- 为了在勘探和开采之间取得稳健的平衡.
主要方法:
- 黑翼算法 (BKA) 与粒子群优化 (PSO) 和差异进化 (DE) 的混合.
- 整合地方利用的公用事业任务和维持人口多样性的DE.
- 通过BKA的战略进行动态的全球探索,通过PSO的基于速度的搜索进行本地改进.
主要成果:
- BKAPI在合速度和计算精度方面取得了显著的改进.
- 改进的算法有效地克服了过早的收和参数灵敏度.
- 使用CEC 2017和CEC 2022基准函数的实验验证显示其优于其他七种算法.
结论:
- 拟议的BKAPI为高维优化问题提供了强大的解决方案.
- 在工程设计中,BKAPI表现出强大的性能和广泛的适用性.
- 综合策略证明了对复杂的优化任务的有效性和多功能性.
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