对全球连续优化问题进行分层指导的鱼食优化以及太阳能光伏模型的参数估计
Zhentao Tang1,2,3, Kaiyu Wang4, Lan Zhuang1
1Jiangsu Agri-animal Husbandry Vocational College, Taizhou, 225300, China.
这项研究介绍了层次引导的鱼食优化 (HGMRFO),这是一个改进的算法,可以克服局部最佳问题. 在复杂的优化任务中,HGMRFO提高了勘探-开发平衡,以提高性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式计算 超启发式计算
背景情况:
- 曼塔射线食优化 (MRFO) 算法对工程问题是有效的,但受到了局部最佳和不良的勘探-开发平衡的影响.
- 固定参数和专注于当前最佳解决方案的跳转机制限制了MRFO的适应性.
研究的目的:
- 为了提出一个改进的优化算法,等级引导的曼塔射线食优化 (HGMRFO).
- 解决MRFO的局限性,特别是其陷入局部最佳状态的倾向以及勘探和开采之间的不充分平衡.
主要方法:
- 引入了自适应式跳跃因子,以动态平衡勘探和开发.
- 开发了一种新的等级指导机制,在转食策略中用于直接搜索人口.
- 在IEEE CEC2017基准函数和IEEE CEC2011现实世界问题上对七个最先进的算法进行了验证HGMRFO.
主要成果:
- 在29个IEEE CEC2017基准函数中,HGMRFO实现了73.15%的平均胜率.
- 在22个IEEE CEC2011现实世界的优化问题上获得了最优化的解决方案.
- 在多式联网光伏模型的参数估计中显示了97.62%的成功率.
结论:
- 与标准MRFO相比,HGMRFO有效地克服了局部最佳问题,并改善了勘探开发平衡.
- 提出的层次指导机制和适应参数显著提高了优化性能.
- HGMRFO显示出卓越的适用性和有效性,特别是在解决复杂的问题,如光伏参数估计.
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