# A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation
## 6 Conclusions and Future Work  
In order to enhance the search efficiency, convergence accuracy, and stability of SSA, a novel adaptive sparrow search algorithm based on integrating the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy, namely the CWTSSA, was proposed in this paper. The CWTSSA uses a sine chaotic mapping strategy to initialize the population in order to enrich the diversity of solutions and speed up the exchange of information between populations. The adaptive weighting strategy and the t-distribution mutation strategy broaden the search area of the sparrow, effectively improve the local development ability and global search ability, so as to improve the convergence accuracy of the algorithm, enhance the stability of the algorithm, and enable the algorithm to avoid falling into the local optimum. The effectiveness of the CWTSSA was tested on 12 benchmark functions. For functions \(f_{1}-f_{15}\), the proposed CWTSSA achieved better results than a single strategy and combination strategy. At the same time, the CWTSSA performed well, and could even converge to the global optimal value under different dimensions in comparison with GWO, PSO and SSA. The performance was equivalent in global optimization, and the optimal value could be obtained by Wilcoxon rank and tests. To sum up, when the three strategies were used at the same time for SSA, most of the test functions had better convergence speed and convergence accuracy, and the stability was also better. This is inseparable from the use of the three strategies. In general, the SSA with each strategy shows a certain improvement in comparison with the basic SSA. From the experimental results, it can be seen that the CWTSSA had stronger optimization performance in terms of optimization accuracy, convergence speed, stability, etc.  
In the next step, the CWTSSA will be applied to practical engineering problems such as image segmentation, face recognition, constraint optimization, multi-objective optimization, etc. [43]. Therefore, the aim will be to verify the ability and superiority of the CWTSSA in solving actual engineering problems, such as path planning, resource allocation, etc.  
Methodology, X.Y.; software, Y.L.; validation, J.L., X.Y. and P.X.; formal analysis, X.Y.; investigation, J.L. and L.Y.; resources, W.D. and L.Y.; writing--original draft preparation, X.Y.; writing--review and editing, H.C. and L.Z.; visualization, P.X. and L.Z; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China(U2033214), the China National Key R&D Program (2018 YFB1601200), and the Research Foundation for Civil Aviation University of China (2020 KYQD123), Research and Innovation Project for Postgraduates in Tianjin (Artificial Intelligence) (2020 YJSZXS33).  
Not applicable.  
The authors would like to thank all the reviewers for their constructive comments. The program for the initialization, study, training, and simulation of the proposed algorithm in this article was written with the tool-box of MATLAB 2019b produced by Math-Works, Inc.  
The authors declare no conflict of interest.