This study presents a novel implementation of evolutionary heuristics through backtracking search optimization algorithm (BSA) for accurate, efficient and robust parameter estimation of power signal models. The mathematical formulation of fitness function is accomplished by exploiting the approximation theory in mean squared errors between actual and estimated responses, as well as, true and approximated decision variables. Variants of BSA-based meta-heuristics are applied for parameter estimation problem of power signals for identification of amplitude, frequency and phase parameters for different scenarios of noise variation. Analysis of performance evaluation for BSAs is conducted through exhaustive statistical observations in terms of mean weight deviation, root mean square error and Thiel inequality coefficient-based assessment metrics, as well as, ANOVA tests for statistical significance.

这项研究提出了一种通过回溯搜索优化算法（BSA）对功率信号模型进行准确，高效和鲁棒性参数估计的进化启发式算法的新实现。适应度函数的数学公式是通过利用逼近理论来计算实际和估计响应之间的均方误差以及真实和近似决策变量而完成的。基于BSA的元启发式算法的变体被应用于功率信号的参数估计问题，以识别不同噪声场景下的幅度，频率和相位参数。BSA的性能评估分析是通过详尽的统计观察进行的，包括平均权重偏差，均方根误差和基于Thiel不平等系数的评估指标，