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改进型麻雀搜索算法及其在 HIFU 声场中的应用

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

机构信息

Department of Electronic Engineering and Information, University of Science and Technology of China, Hefei, Anhui 230026, China.

出版信息

Comput Intell Neurosci. 2023 Mar 3;2023:1228685. doi: 10.1155/2023/1228685. eCollection 2023.

DOI:10.1155/2023/1228685
PMID:36909963
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10005866/
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.

摘要

麻雀搜索算法(SSA)是一种新颖的群体智能优化算法。它具有快速收敛速度和强大的全局搜索能力。然而,SSA 也有许多缺点,例如初始种群的质量不稳定,容易陷入局部最优解,以及种群的多样性随着迭代过程而减少。为了解决这些问题,本文提出了一种改进的麻雀搜索算法(ISSA)。ISSA 使用 Chebyshev 混沌映射和精英基于对立的学习策略来初始化种群,从而提高初始种群的质量。在生产者位置更新过程中,引入动态权重因子和 Levy 飞行策略,以避免陷入局部最优解。突变策略应用于觅食者位置更新过程,对个体进行突变操作,以增加种群的多样性。为了验证 ISSA 的可行性和有效性,在 23 个基准函数上进行了测试。结果表明,与其他七种算法相比,ISSA 具有更高的收敛精度、更快的收敛速度和更强的稳定性。最后,ISSA 被用于优化高强度聚焦超声(HIFU)声场。结果表明,ISSA 可以有效地提高聚焦性能,降低声场旁瓣的影响,这对 HIFU 治疗非常有益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/15b444b8398e/CIN2023-1228685.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/c84bfa2acd8e/CIN2023-1228685.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/bd50e35bdad2/CIN2023-1228685.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/69d1c2fca2f4/CIN2023-1228685.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/65655bb7f7fe/CIN2023-1228685.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/37ac849e0351/CIN2023-1228685.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/d63b01e93ccd/CIN2023-1228685.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/8fd972d14780/CIN2023-1228685.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/15b444b8398e/CIN2023-1228685.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/c84bfa2acd8e/CIN2023-1228685.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/bd50e35bdad2/CIN2023-1228685.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/69d1c2fca2f4/CIN2023-1228685.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/65655bb7f7fe/CIN2023-1228685.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/37ac849e0351/CIN2023-1228685.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/d63b01e93ccd/CIN2023-1228685.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/8fd972d14780/CIN2023-1228685.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1727/10005866/15b444b8398e/CIN2023-1228685.alg.001.jpg

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