PERMMA:提高软件可靠性增长模型的参数估计:对元启发式优化算法的比较分析
Vishal Pradhan1, Arijit Patra1, Ankush Jain2
1School of Applied Sciences, Kalinga Institute of Industrial Technology, Odisha, India.
PloS one
|September 4, 2024
概括
本研究探讨了软件可靠性增长模型 (SRGM) 的元启发式优化. 再生基因算法 (RGA) 和灰狼优化器 (GWO) 显示了SRGM的优越参数估计能力.
科学领域:
- 软件工程 软件工程 软件工程
- 可靠性工程可靠性工程
- 优化算法 优化算法
背景情况:
- 软件可靠性增长模型 (SRGM) 对于评估软件可靠性至关重要.
- 传统的参数估计方法,如最大概率估计 (MLE) 和最小平方估计 (LSE) 有局限性.
- 超启发式优化算法为克服参数估计中的这些局限性提供了先进的解决方案.
研究的目的:
- 分析元启发式算法在SRGM中的参数估计中的适用性.
- 为了比较四个元启发算法的性能:灰狼优化器 (GWO),再生遗传算法 (RGA),正弦直算法 (SCA) 和引力搜索算法 (GSA).
- 通过使用已建立的SRGMs对实际软件故障数据进行评估这些算法.
主要方法:
- 四个元启发算法 (GWO,RGA,SCA,GSA) 用于参数估计.
- 用四个流行的SRGM和三个现实世界的故障数据集进行了比较分析.
- 绩效是根据收标准和R2分布来评估的.
主要成果:
- 听算法产生了接近LSE值的参数估计.
- 在各种真实世界的故障数据集中,RGA和GWO表现出卓越的性能.
- 与GWO和其他方法相比,RGA在定位最佳解决方案方面显示了更快的趋同和更高的准确性.
结论:
- RGA和GWO非常适合在SRGM中进行参数估计.
- 建议使用再生遗传算法 (RGA),因为它在优化SRGM参数方面的效率和准确性.
- 超启发式方法为增强软件可靠性分析提供了强大的替代方案.
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