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EBBO:一个生物模拟增强的优化算法,为复杂的工程应用提供多阶段合作

Xuemei Zhu¹, Haoyu Cai², Shirong Li³

¹Experimental Training Teaching Management Department, West Anhui University, Yu'an District, Lu'an 237012, China.

Biomimetics (Basel, Switzerland)

|February 26, 2026

概括

增强的Beaver行为优化器 (EBBO) 通过整合适应突变和动态学习来改善复杂的问题解决. 这种新的优化算法在工程应用中展示了卓越的性能和稳定性.

科学领域:

计算智能是一种计算智能.
优化算法优化算法
工程应用工程应用

背景情况:

原来的Beaver行为优化器 (BBO) 在解决复杂的优化问题方面存在局限性.
需要先进的优化技术,有效平衡勘探和开采.

研究的目的:

引入增强的Beaver行为优化器 (EBBO) 来解决原始BBO算法的局限性.
评估EBBO在复杂,高维和多式优化问题上的表现.

主要方法:

EBBO集成了一个具有适应性突变的三阶段合作框架.
结合了基于对立的动态学习和受模拟回火启发的风险意识决策策略.
使用CEC 2017和CEC 2020基准套件和经典工程设计问题来评估性能.

主要成果:

在收准确性,稳定性和稳定性方面,EBBO显著优于包括原始BBO在内的九种广泛使用的算法.
与BBO相比,证明了15-50%的平均目标值降低和30-70%的标准偏差降低.
实现了工程设计问题的最佳或近最佳解决方案,如步形圆滑轮,压力容器,三条结构结构优化和3D无人机路径规划,满足所有约束.

结论:

EBBO提供了一种可靠和高效的方法来解决复杂的受约束优化挑战.
该算法有效地平衡了勘探和开发,在基准和现实世界的工程环境中实现了卓越的性能.
EBBO代表了优化算法开发的重大进步.

关键词:

贝弗行为优化器有约束的优化优化.工程优化设计设计工程优化设计增强的优化算法优化算法这种算法是Metaheuristic算法.

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