## 3 Improved Sparrow Search Algorithm
### Improved Sparrow Search Algorithm
From all the results above, it is demonstrated that the proposed method has high prediction accuracy, dramatically improves the time-consuming aspect, and can quickly design the antenna to meet the requirements. ## 6 Conclusions

This paper presents a design method for the multi-objective antenna, which can be used to design the antenna quickly. This method is implemented using an improved SSA to optimize the surrogate model. First, the sparrow search algorithm is improved by using three strategies, namely the chaotic map, inertia weight, and t-distribution, to enhance its searchability and optimization performance. Secondly, the proposed new algorithm is applied to the original neural network, and the surrogate model based on MISSA-BP is constructed. Finally, the surrogate model is united with the MOPSO algorithm to resolve the difficulties in multi-objective antenna design. The relevant results state that MISSA performs better, and this MISSA-BP model can accurately predict the performance of the antenna. The accuracy is significantly improved and less time-consuming. This method has good practical value and significance for the design of the multi-objective antenna. Not applicable. The dataset generated and analyzed during the current study is available from the corresponding author on reasonable request. The authors declare no conflict of interest. ## References

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