黑翼风算法集成基于对立的学习和准牛顿的战略
Ning Zhao1, Tinghua Wang1, Yating Zhu1
1Key Laboratory of Data Science and Artificial Intelligence of Jiangxi Education Institutes, Gannan Normal University, Ganzhou 341000, China.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
这项研究通过基于对立的学习和准牛顿方法 (OQBKA) 增强了黑翼风算法 (BKA),以改善全球搜索和多样性. 在基准函数和工程问题上,OQBKA表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超听证学是一种超听证学.
背景情况:
- 黑翼风算法 (BKA) 受到全球搜索能力有限和人口多样性下降的影响.
- 解决这些局限性对于提高算法在复杂的优化任务中的有效性至关重要.
研究的目的:
- 通过整合基于对立的学习和准牛顿的策略,提出一个增强的黑翼风算法 (OQBKA).
- 改善全球搜索能力,人口多样性,本地优化精度和BKA的融合性能.
主要方法:
- 引入了基于凸透镜成像 (MOBL) 的镜像成像策略,以提高人口分布和局部最佳逃生.
- 在后来的代中纳入准牛顿方法,以改善本地优化和融合.
- 在CEC2017基准准组上进行了废除研究.
- 在CEC2022基准套件上进行了比较实验.
- 在三个受约束的工程设计问题上评估了性能.
主要成果:
- 在CEC2017基准测试中,OQBKA在29个测试函数中获得了1.34的平均排名.
- 在CEC2022基准中,OQBKA获得了2.5 (10维) 和2.17 (20维) 的平均排名,超过了十个最先进的算法.
- 证明了卓越的勘探开发平衡和优化准确性.
- 成功地为复杂的受限制的工程设计问题生成可行的解决方案.
结论:
- 在OQBKA的综合战略显示强大的互补性,显著提高了BKA的表现.
- OQBKA提供了一种强大而实用的方法来解决复杂的优化问题,包括工程设计.
- 拟议的OQBKA与现有的元启发算法相比,具有更高的性能.
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