一个增强的随机化虫优化器,用于全球优化问题
Hui Yu1, Mengyuan Xie2, Zhanxi Zhou3
1The School of Computer Engineering, Hubei University of Arts and Science, Xiangyang 441053, China.
Biomimetics (Basel, Switzerland)
|November 26, 2025
概括
增强繁殖虫优化器 (ERDBO) 通过解决过早的融合和准确性问题,改进了标准的虫优化器 (DBO). 这种新的算法为复杂的工程优化问题提供了一个强大的框架.
科学领域:
- 优化算法 优化算法
- 计算智能是一种计算智能.
- 超听证学是一种超听证学.
背景情况:
- 标准的Dung Beetle Optimizer (DBO) 显示了复杂优化的潜力,但在过早的融合和准确性方面面临挑战.
- 现有的元启发式方法往往难以有效地平衡全球勘探和本地开发.
研究的目的:
- 引入增强的繁殖虫优化器 (ERDBO),以克服传统DBO的局限性.
- 在优化任务中提高融合率,稳定性和解决方案精度.
主要方法:
- ERDBO采用了一种新的三阶段机制:幼虫生长与多样性的体验学习,繁殖与父母-后代验证的剥削,和掠食者避开的勒维飞行适应性.
- 使用CEC2017基准函数评估算法性能,并与先进的元启发式方法进行比较.
- 该ERDBO被应用于工程设计问题,包括张力/压缩弹,三条螺纹和压力容器.
主要成果:
- 与其他先进算法相比,ERDBO在融合率,稳定性和解决方案精度方面表现出卓越的性能.
- 对基准函数的实验结果证实了ERDBO的有效性.
- 对工程设计任务的成功应用验证了它的效率和实际应用.
结论:
- 与DBO相比,ERDBO提供了显著的进步,有效地减轻了过早的趋同,提高了准确性.
- 拟议的算法提供了一个强大的和具有竞争力的优化框架,适合复杂的现实世界工程挑战.
- ERDBO的混合方法提高了适应性,加速了融合,使其成为计算优化中的一个有价值的工具.
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