层次化的多步骤灰狼优化算法用于能源系统优化优化
Idriss Dagal1, Al-Wesabi Ibrahim2, Ambe Harrison3,4
1Electrical Engineering, Beykent University, Ayazağa Mahallesi, Hadım Koruyolu Cd. No:19, Sarıyer, Istanbul, Turkey. idriss.dagal@std.yildiz.edu.tr.
层次多步灰狼优化 (HMS-GWO) 增强了标准的灰狼优化 (GWO) 算法. 对于复杂的优化问题,HMS-GWO可以提高合速度和解决方案的准确性.
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:
- 灰狼优化 (GWO) 是一种由狼群行为启发的元启发算法.
- 标准GWO面临着诸如过早收和参数灵敏度等挑战.
- 现有的GWO变种并不能完全捕捉狼群的等级结构.
研究的目的:
- 为了引入层次的多步骤灰狼优化 (HMS-GWO) 算法.
- 解决标准GWO的局限性,如过早的融合和停滞.
- 在优化中增强探索,开发和解决方案多样性.
主要方法:
- 开发了一个新的层次决策框架的HMS-GWO.
- 模仿分层的狼群行为,对每个狼类型 (阿尔法,贝塔,三角形,欧米茄) 进行结构化的多步骤搜索过程.
- 在一个由23个功能组成的基准套件上评估性能.
主要成果:
- HMS-GWO实现了99%的准确性,计算时间为3秒,稳定性得分为0.9.
- 与标准GA,PSO,MMSCC-GWO,WCA和CCS-GWO相比,表现明显更好.
- 展示了更快的融合和改进的解决方案准确性,缓解过早的融合问题.
结论:
- HMS-GWO有效地克服了标准GWO的局限性.
- 层次化的方法提高了解决复杂优化问题的稳定性和效率.
- 对于需要高级优化的各种应用领域,HMS-GWO是一个有前途的替代方案.
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