School of Applied Sciences, Kalinga Institute of Industrial Technology, Odisha, India.
Department of Computer Science & Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi, India.
PLoS One. 2024 Sep 4;19(9):e0304055. doi: 10.1371/journal.pone.0304055. eCollection 2024.
Software reliability growth models (SRGMs) are universally admitted and employed for reliability assessment. The process of software reliability analysis is separated into two components. The first component is model construction, and the second is parameter estimation. This study concentrates on the second segment parameter estimation. The past few decades of literature observance say that the parameter estimation was typically done by either maximum likelihood estimation (MLE) or least squares estimation (LSE). Increasing attention has been noted in stochastic optimization methods in the previous couple of decades. There are various limitations in the traditional optimization criteria; to overcome these obstacles metaheuristic optimization algorithms are used. Therefore, it requires a method of search space and local optima avoidance. To analyze the applicability of various developed meta-heuristic algorithms in SRGMs parameter estimation. The proposed approach compares the meta-heuristic methods for parameter estimation by various criteria. For parameter estimation, this study uses four meta-heuristics algorithms: Grey-Wolf Optimizer (GWO), Regenerative Genetic Algorithm (RGA), Sine-Cosine Algorithm (SCA), and Gravitational Search Algorithm (GSA). Four popular SRGMs did the comparative analysis of the parameter estimation power of these four algorithms on three actual-failure datasets. The estimated value of parameters through meta-heuristic algorithms are approximately near the LSE method values. The results show that RGA and GWO are better on a variety of real-world failure data, and they have excellent parameter estimation potential. Based on the convergence and R2 distribution criteria, this study suggests that RGA and GWO are more appropriate for the parameter estimation of SRGMs. RGA could locate the optimal solution more correctly and faster than GWO and other optimization techniques.
软件可靠性增长模型（SRGMs）被广泛认可并用于可靠性评估。软件可靠性分析的过程分为两个部分。第一部分是模型构建，第二部分是参数估计。本研究集中于第二部分的参数估计。过去几十年的文献观察表明，参数估计通常通过最大似然估计（MLE）或最小二乘估计（LSE）来完成。在过去的几十年中，随机优化方法引起了越来越多的关注。传统优化标准存在各种局限性；为了克服这些障碍，使用了元启发式优化算法。因此，它需要一种搜索空间和避免局部最优的方法。为了分析各种已开发的元启发式算法在 SRGM 中参数估计的适用性。本研究使用了四种元启发式算法：灰狼优化器（GWO）、再生遗传算法（RGA）、正弦余弦算法（SCA）和引力搜索算法（GSA），提出了一种通过各种标准比较参数估计中元启发式方法的方法。对于参数估计，本研究使用了四种元启发式算法：灰狼优化器（GWO）、再生遗传算法（RGA）、正弦余弦算法（SCA）和引力搜索算法（GSA）。在四个流行的 SRGM 上，对这四个算法在三个实际失效数据集上的参数估计能力进行了比较分析。通过元启发式算法估计的参数值与 LSE 方法的值相近。结果表明，RGA 和 GWO 在各种实际失效数据上表现更好，具有优异的参数估计潜力。根据收敛性和 R2 分布标准，本研究建议 RGA 和 GWO 更适合 SRGM 的参数估计。RGA 比 GWO 和其他优化技术更能正确和快速地找到最优解。
