修改的黑翼优化算法与三相攻击策略和莱维-考西迁移行为来解决数学问题
Yunpeng Ma1, Wanting Meng2, Ruixue Gu1
1School of Information Engineering, Tianjin University of Commerce, Beichen, Tianjin 300134, China.
Biomimetics (Basel, Switzerland)
|October 28, 2025
概括
修改后的黑翼算法 (MBKA) 通过改善种群多样性和平衡勘探来提高优化. 这种新的算法在基准和工程问题上表现优于原来的BKA.
科学领域:
- 计算智能是一种计算智能.
- 启发式优化算法 启发式优化算法
背景情况:
- 黑翼算法 (BKA) 是最近的一种启发式优化方法.
- 现有的算法需要进一步改进融合准确性和解决方案质量.
研究的目的:
- 引入一个修改的黑翼风算法 (MBKA).
- 提高BKA的趋同精度和解决方案质量.
主要方法:
- 制定了三阶段的攻击策略,以促进人口多样性.
- 整合了莱维-考西迁移战略,以改善勘探和开采的平衡.
- 对基准函数 (CEC-2017,CEC-2022) 和工程问题进行评估的MBKA.
主要成果:
- MBKA 证明了增强的人口多样性和解决方案质量.
- 莱维-考西战略改善了勘探-开采权衡.
- MBKA始终超过了原来的BKA和其他最先进的算法.
结论:
- 在融合准确性和速度方面,MBKA提供了卓越的性能.
- 拟议的修改大大提高了BKA的优化能力.
- 对于复杂的优化任务,MBKA是一个有前途的算法.
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