Metaheuristic algorithms (MAs) have become one of the primary tools for optimization in diverse domains, including non-invasive fetal electrocardiogram (fECG) extraction. Research reveals that hyperparameters affect the performance of MAs differently to problems, and some algorithms are problem-specific designed and may produce bad results for different problems. Hence, three questions arise, (1) how much can we trust MAs when solving boxed-constrained fECG extraction problems, and (2) which type of MAs are suitable and adequate for fECG extraction? (3) do MAs find acceptable solutions to a problem that does not formulate the problem comprehensively with an imperfect objective function? This paper focuses on these three inquiries and proposes a framework providing an MA-assisted adaptive filter for non-invasive fECG extraction. The proposed framework has three key components: data pre-processing, MA-based adaptive filter, and post-processing. The pre-processing starts to process the signals detected by pregnant women and extract the desired signal by independent component analysis. Such output signals are then passed to a filter and optimized by population-based algorithms, producing an optimal solution. After that, this solution, as filter weights, will be used for signal extraction and be processed by post-processing. Importantly, the proposed framework is user-friendly that can import any MA algorithm to run and disassemble as a separate assisting tool. This work investigated eight classic and disparate MA algorithms on the Abdominal and Direct Fetal ECG Database (ADFECGDB) dataset, offering comprehensive experimental analysis. We infer from the results that the recordings r01, r02, r03, r05, r08, and r09, where the signals are less noisy and can be solved well. While for recordings r04, r07, r06, and r10, the performance can be influenced by hyperparameters for any test algorithms, such as the population size, window size, and other parameters in MAs. Meanwhile, we found that MA may not be the primary target influencing the performance vary.