基于人口多样性控制的差异进化算法,使用模糊系统来解决杂的多目标优化问题
Brindha Subburaj1, J Uma Maheswari2, S P Syed Ibrahim2
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India. brindha.s@vit.ac.in.
Scientific reports
|August 1, 2024
概括
本研究介绍了一种基于差异进化的噪音处理优化算法 (NDE),以有效解决杂的双目标问题. 通过自适应参数和采用否定方法,NDE提高了性能,在基准测试中表现优于现有的算法.
科学领域:
- 优化算法 优化算法
- 计算智能是一种计算智能.
- 工程优化工程优化
背景情况:
- 现实世界的优化问题往往涉及噪音测量影响算法性能.
- 现有的噪音处理方法可能是计算上昂贵或问题特定的.
- 开发用于噪音双目标优化的强大的算法对于实际应用至关重要.
研究的目的:
- 为杂的双目标问题提出基于差异进化的新噪声处理优化算法 (NDE).
- 增强人口多样性,改善噪音环境中的融合特征.
- 解决现有方法在计算成本和问题特异性方面的局限性.
主要方法:
- 开发了一个基于差异进化 (DE) 的算法 (NDE).
- 模糊推理系统用于DE控制参数和试验矢量生成策略的自适应.
- 对于高噪音水平,还采用了基于平均值的明确无声化方法.
- 实施局部搜索限制程序以改善收.
主要成果:
- 在噪音条件下,NDE算法在基准双目标问题 (DTLZ和WFG) 上表现出卓越的性能.
- 与使用逆代距离和超量度指标的最先进的算法进行比较,证实了NDE的有效性.
- 统计测试 (Wilcoxon,弗里德曼等级) 显示,NDE比其他方法提供了显著的改进.
结论:
- 拟议的NDE算法有效地处理双目标优化问题的噪声.
- NDE的适应性策略和无化方法带来了更好的性能和稳定性.
- 临近体验代表了解决现实世界噪音优化挑战的重大进展.
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