一个增强的秘书鸟优化算法基于数值优化问题的多种群管理
Jin Zhu1, Bojun Liu2, Jun Zheng3
1School of Journalism and Communication, Tsinghua University, Beijing 100000, China.
Biomimetics (Basel, Switzerland)
|November 26, 2025
概括
本研究介绍了以多种群体体验趋势为导向的秘书鸟优化算法 (MESBOA),以增强基于群体的优化. MESBOA克服了原始算法的局限性,在准确性和收速度方面表现出卓越的性能.
科学领域:
- 计算智能是一种计算智能.
- 群集情报 群集情报 群集情报
- 优化算法 优化算法
背景情况:
- 秘书鸟类优化算法 (SBOA) 是一种由鸟类行为启发的新元启发式算法.
- 现有的SBOA在勘探-开发平衡,人口多样性和当地最佳状态方面存在挑战.
- 这些局限性阻碍了它在复杂的优化任务中的有效性.
研究的目的:
- 提出一个增强的秘书鸟优化算法 (MESBOA).
- 解决原来的SBOA的缺点,包括不平衡的勘探开发和过早的融合.
- 为了提高算法的性能在基准和现实世界的优化问题.
主要方法:
- 将多种人口管理战略集成到SBOA中.
- 整合一个经验趋势指导策略来指导搜索过程.
- 在CEC2017和CEC2022测试套件上对八种先进算法的比较分析.
主要成果:
- 在CEC测试套件上,MESBOA在各种维度 (10-D,20-D,50-D,100-D) 上实现了卓越的性能.
- 与现有算法相比,证明了更快的融合,更强大的稳定性和更高的准确性.
- 验证了对现实世界的工程受约束优化问题的适用性.
结论:
- MESBOA有效地克服了标准SBOA的局限性.
- 拟议的改进将大大提高优化效率和解决方案质量.
- 在复杂的优化场景中,MESBOA显示出强大的实际应用潜力.
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