## 3 Advanced AFSA Optimization Method
### Summary  
In the indoor localization systems, if the upgrading strategies mentioned above are added, it will effectively improve the NLOS identification effect. Then, it tells the user whether the location is appropriate through more accurate positioning results. In detail, the re-selection and re-organization process on these branch samples for the existing results will be completed, thus adjusting the main process precisely based on CTGSA-AFSA, at the same time retaining the probability of elaborating the established learning direction or tendency in the system. In addition, they will directly guarantee that acoustic signal samples can obtain more reasonable training weights under different NLOS scenarios. It will make the overall NLOS coping mechanism more comprehensive. This can also be reflected in the randomness of the sample movement process. Meanwhile, the methods will make sure that the physical strength transformation model value seldom changes to zero. According to different scenarios in the system, the flexible samples will further improve the performance of CTGSA-AFSA. As for other AFSA optimization methods, although many of them may have achieved considerable results, it does not mean that they can be directly used in the core part of this system and complete the identification of NLOS acoustic signals in the dual-receiver structure anchors in our flexible NLOS situations. In fact, these updating strategies on AFSA need to balance two factors: one is the accuracy, and the other is how much time it takes to complete the training. In some scenarios, the acoustic signal data set in an offline state can be adopted to complete the necessary training and then be used in multiple scenarios. However, this does not mean that the results of offline data set training for static NLOS acoustic localization signals must be applied to complex and flexible NLOS scenarios. Accordingly, excellent schemes from the recent literature [39; 40; 41] are used for comparison. The data set used is from the literature [22] in this part. During its acoustic signal data-building process, those static NLOS scenarios in different situations are produced. In order to facilitate comparison, on the premise of using the same data set, the upgrade methods are used to upgrade SVM to compare the effects of AFSA optimization methods. For a more rigorous comparison, 20 independent experiments are conducted for each group, then the overall results are counted and the average value of each experiment result is calculated. Our method is compared with the methods mentioned above for upgrading the SVM, evaluate the effect, and the specific results are showed in Table 1:  
Figure 5: CTGSA main optimization method. Among these methods, the identification accuracy of some methods obtained may be better than the methods used in this paper, but their training times were much longer than those of the methods used in this paper. In the static NLOS scenario [20,22], the accuracy should be paid more attention than the training time. However, in the dynamic NLOS scenarios, the RSS selecting mechanism processed from [22] may be used too frequently due to their long training time, which may lead to uncontrollable and serious consequences. Therefore, researchers must better balance the relationship between accuracy and training time in flexible NLOS situations. In the system involved in this paper, it is also likely to coordinate the final effect in the real-world situation due to the mechanism or solutions from [22]. When designing and deploying the indoor localization system, it is necessary and wise to achieve the necessary balance according to the actual situation of this site when selecting the AFSA optimization scheme in indoor sensor systems. CTGSA time is not included in the above statistical results. This is because this process mainly affects the initialization stage, which can be used in an offline state, and is also one of the advantages of our method.