Multiple-Focus Patterns of Sparse Random Array Using Particle Swarm Optimization for Ultrasound Surgery

Zhang, Quan; Mao, Jianyun; Zhang, Yi; Lu, Minghui; Li, Ruixin; Li, Xuan; Liu, Yehui; Yang, Rongzheng; Wang, Xuan; Geng, Yizhe; Qi, Tingting; Wan, Mingxi

doi:10.1109/tuffc.2021.3127222

Cited by 4 publications

(1 citation statement)

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“…By applying the binary particle swarm optimization (BPSO) algorithm to the total focusing method (TFM) [ 15 ] for thinned array design, the simulation results indicate that the proposed TFM can greatly increase computational efficiency and provide significantly higher image quality. The PSO study in [ 16 ] aims to generate multiple-focus patterns and a large scanning range for random arrays thinning, which is applied to the ultrasound treatment of brain tumors and neuromodulation. A search mode with multi-objective particle swarm optimization was proposed in [ 17 ] to solve the problem of optimal array distribution, which met the requirements of reducing the number of antenna elements and maintaining the PSLL simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Constrained Planar Array Thinning Based on Discrete Particle Swarm Optimization with Hybrid Search Strategies

Cai

Guo

et al. 2022

Sensors

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This article presents a novel optimization algorithm for large array thinning. The algorithm is based on Discrete Particle Swarm Optimization (DPSO) integrated with some different search strategies. It utilizes a global learning strategy to improve the diversity of populations at the early stage of optimization. A dispersive solution set and the gravitational search algorithm are used during particle velocity updating. Then, a local search strategy is enabled in the later stage of optimization. The particle position is adaptively adjusted by the mutation probability, and its motion state is monitored by two observation parameters. The peak side-lobe level (PSLL) performance, effectiveness and robustness of the improved PSO algorithm are verified by several representative examples.

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Section: Introductionmentioning

confidence: 99%