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. 2023 Mar 3:2023:1228685.
doi: 10.1155/2023/1228685. eCollection 2023.

An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field

Yihao Yang  1 , Jinxu Tao  1 , Jiayu Zhou  1 , Jiaqi Wang  1 , Xinyu Guo  1
Affiliations

An Improved Sparrow Search Algorithm and Its Application in HIFU Sound Field

Yihao Yang et al. Comput Intell Neurosci. .

Abstract

The sparrow search algorithm (SSA) is a novel swarm intelligence optimization algorithm. It has a fast convergence speed and strong global search ability. However, SSA also has many shortcomings, such as the unstable quality of the initial population, easy to fall into the local optimal solution, and the diversity of the population decreases with the iterative process. In order to solve these problems, this paper proposes an improved sparrow search algorithm (ISSA). ISSA uses Chebyshev chaotic map and elite opposition-based learning strategy to initialize the population and improve the quality of the initial population. In the process of producer location update, dynamic weight factor and Levy flight strategy are introduced to avoid falling into a local optimal solution. The mutation strategy is applied to the scrounger location update process, and the mutation operation is performed on individuals to increase the diversity of the population. In order to verify the feasibility and effectiveness of ISSA, it is tested on 23 benchmark functions. The results show that compared with other seven algorithms, ISSA has higher convergence accuracy, faster convergence speed, and stronger stability. Finally, ISSA is used to optimize the sound field of high-intensity focused ultrasound (HIFU). The results show that ISSA can effectively improve the focusing performance and reduce the influence of sound field sidelobe, which is of great benefit for HIFU treatment.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Concave spherical phased array transducer model: (a) side view and (b) main view.
Figure 2
Figure 2
Convergence curves of eight algorithms on benchmark functions. (a) F1. (b) F2. (c) F3. (d) F4. (e) F5. (f) F6. (g) F7. (h) F8. (i) F9. (j) F10. (k) F11. (l) F12. (m) F13. (n) F14. (o) F15. (p) F16. (q) F17. (r) F18. (s) F19. (t) F20. (u) F21. (v) F22. (w) F23.
Figure 3
Figure 3
Convergence curves of different improvement strategies on benchmark functions: (a) F2, (b) F5, (c) F7, (d) F9, (e) F12, (f) F13, (g) F14, and (h) F21.
Figure 4
Figure 4
Sound field distribution on z = 100 mm plane with the symmetric focal point: (a) unoptimized and (b) ISSA-optimized.
Figure 5
Figure 5
Variation curve of sound pressure P with y on the axis of x = −10 mm.
Figure 6
Figure 6
Sound field distribution on z = 200 mm plane with the asymmetric focal point: (a) unoptimized and (b) ISSA-optimized.
Figure 7
Figure 7
Variation curve of sound pressure P with y on the axis of x = −20 mm.
Algorithm 1
Algorithm 1
The improved sparrow search algorithm.
See this image and copyright information in PMC

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