对于功能优化和工程问题而言,改进了红蓝的优化
Chi Han1, Tingwei Zhang2, Huimin Han1
1School of Mechanical and Electrical Engineering, Hainan Vocational University of Science and Technology, Haikou 571126, China.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
一个改进的红蓝优化 (IRBMO) 算法通过整合精英指导,差异进化和准对立式学习来提高元启发性性能,以克服过早融合等局限性.
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:
- 红蓝优化 (RBMO) 算法是一种新的元启发.
- RBMO面临着诸多挑战,包括过早的融合和不平衡的勘探开发.
- 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:
- 引入一个改进的红蓝优化 (IRBMO) 算法.
- 为了解决原始RBMO算法的固有局限性.
- 为了提高功能优化和工程问题的性能.
主要方法:
- 在多个人口的合作框架内整合三个协同战略.
- 增强RBMO搜索与精英指导加速融合.
- 适应差异演变运算符用于改进本地搜索和逃离本地最佳值.
- 基于准对立的学习,以促进全球探索和人口多样性.
主要成果:
- 对26个经典基准函数和现实世界的工程设计问题进行严格的评估.
- 与原来的RBMO相比,IRBMO表现明显优越.
- 在解决方案准确性,融合速度和稳定性方面,IRBMO在其他领先的优化算法中表现出色.
结论:
- 拟议的IRBMO算法有效地克服了原来的RBMO的局限性.
- 对于解决复杂的优化问题,IRBMO提供了增强的能力.
- 协同策略有助于提高绩效指标和稳定性.
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