一个增强的连接银行系统优化器,为数值优化问题提供多种策略
Yuchen Yin1, Haipeng Liu2, Shanshan Cai3
1Teachers College, Columbia University, 525 West 120Th Street, New York, NY, 10027, USA.
Scientific reports
|March 9, 2026
概括
本研究介绍了ECBSO,这是一个增强的元启发算法,通过解决过早的趋同来改进联网银行系统优化器 (CBSO). 在解决复杂的工程优化问题方面,ECBSO表现出卓越的表现.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 进行元启发式学习.
背景情况:
- 连接银行系统优化器 (CBSO) 是一个用于受约束优化的无参数元启发式.
- CBSO在人口间的沟通和勘探开发平衡方面表现出局限性,导致过早的融合.
- 不充分的搜索空间覆盖阻碍了CBSO在复杂问题上的有效性.
研究的目的:
- 为了增强联网银行系统优化器 (CBSO) 算法.
- 解决CBSO在搜索空间覆盖和过早融合方面的局限性.
- 为工程制约优化问题开发一个强大的和高效的元启发式.
主要方法:
- 引入了一个增强的变体:ECBSO.
- 综合反选择,再生种群和分布估计策略.
- 在CEC-2017基准套件和现实工程问题上进行了实验.
- 采用统计验证,包括威尔科克森等级和,弗里德曼和Nemenyi测试.
主要成果:
- 与现有的算法相比,ECBSO证明了优化效率和稳定性更高.
- 获得的竞争力平均弗里德曼在各种维度 (10D,30D,50D,100D) 中排名.
- 成功解决了十个现实世界的工程受制于优化问题,具有显着的稳定性.
结论:
- ECBSO有效地克服了CBSO的局限性,特别是过早的融合.
- 增强的策略显著提高了搜索空间覆盖率和优化性能.
- ECBSO是一个强大而有效的元启发式变体,适合复杂的工程优化任务.
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