多策略融合改进了北方戈斯霍克优化器,用于工程问题和无人机路径规划
Fan Yang1,2, Hong Jiang1, Lixin Lyu3,4
1School of Information and Artificial Intelligence, Anhui Business College, Anhui, 241002, China.
Scientific reports
|October 7, 2024
概括
多策略集成北戈肖克优化器 (MINGO) 通过改进勘探,开发和融合,增强了原来的北戈肖克优化器 (NGO). MINGO在基准和现实世界问题上表现出卓越的性能.
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:
- 北戈肖克优化器 (NGO) 面临着勘探-开采平衡,缓慢的融合,局部优化和低精度的挑战.
- 解决这些局限性对于开发更有效的优化技术至关重要.
研究的目的:
- 引入多策略集成的北方鱼优化器 (MINGO),以克服原来的非政府组织的局限性.
- 增强勘探战略,开发方法和趋同精度.
- 改进算法的能力,以逃脱当地的最佳状态,并保持人口多样性.
主要方法:
- 整合Levy飞行策略,以保持人口多样性并提高趋同精度.
- 纳入考奇突变策略以减轻局部最佳捕获.
- 应用差异进化的交叉策略,以消除体质不佳的个体,提高人口质量.
主要成果:
- 与非政府组织和其他先进算法相比,MINGO在CEC-2017和CEC-2022基准问题上表现优越.
- 使用Wilcoxon等级总和测试进行的统计分析证实了MINGO的有效性.
- 在六个现实工程问题上的验证和无人机3D轨迹规划展示了MINGO的适用性和优越性.
结论:
- 明戈有效地解决了非政府组织的局限性,在勘探和开采之间实现了更好的平衡.
- 拟议的改进带来了较好的融合速度,精度和与局部最佳相比的稳定性.
- MINGO代表了元启发优化方面的重大进步,在复杂问题上已被证明有效.
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