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对于离散和连续的优化问题,一个改进的破子优化算法:设计,全面分析和工程应用程序
Mohamed Abdel-Basset1, Reda Mohamed1, Ibrahim M Hezam2
1Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt.
Heliyon
|September 25, 2024
概括
一个新的完善的破子优化算法 (INOA) 提高了复杂问题的元启发性能. 在离散和连续的优化任务中,INOA提供更快的融合,并避免局部最小值.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 机器学习应用 机器学习应用
背景情况:
- 传统的优化方法与诸如参数估计和图像分割等复杂问题作斗争.
- 现有的元启发算法经常面临缓慢的融合和局部最小值问题.
- 需要高效的算法用于光伏 (PV) 和质子交换膜燃料电池 (PEMFC) 模型中的参数估计,以及用于多值图像分割.
研究的目的:
- 引入和评估一个改进的破子优化算法 (INOA).
- 解决现有的元启发算法的局限性,即合速度和局部最小值停滞.
- 为了验证INOA在离散和连续搜索空间中对具有挑战性的优化问题的有效性.
主要方法:
- 这项研究提出了INOA,这是一个增强版的破子优化算法,具有新的趋同改进策略.
- INOA适用于单,双和三二极管光伏模型和四个PEMFC模型的参数估计.
- 通过使用各种测试图像,在多门图像分割问题上测试INOA的性能.
主要成果:
- 与几个竞争对手优化器相比,INOA在离散和连续问题上表现出了卓越的性能.
- 对于离散问题,定量改进在0.8355%至3.34%之间,对于连续问题,则在4.97%至99.9%之间.
- 验证指标包括趋同曲线,标准偏差,平均适应性和威尔科克森等级总和测试,证实了INOA的有效性,稳定性和可扩展性.
结论:
- INOA是一种有效和强大的元启发算法,用于解决复杂的离散和连续优化问题.
- 拟议的融合改进战略成功地提高了融合速度,并防止了局部最小值.
- 在PV和PEMFC参数估计和图像细分方面,INOA对现有的方法提供了显著的进步.
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