Related Experiment Video
Updated: Jul 5, 2025
04:48
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
405
psoResNet: An improved PSO-based residual network search algorithm
Dianwei Wang1, Leilei Zhai1, Jie Fang1
1School of Telecommunication and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, PR China.
Summary
This study introduces an enhanced Particle Swarm Optimization for Neural Architecture Search (NAS) to design efficient deep convolutional neural networks (DCNNs). The method optimizes residual networks, achieving superior classification performance with lightweight architectures.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Manual design of deep convolutional neural networks (DCNNs) is time-consuming and costly.
- Existing Neural Architecture Search (NAS) methods face challenges like limited architecture design, long search times, and inefficient search space utilization.
Purpose of the Study:
- To propose an optimized strategy for residual network architecture search using an enhanced Particle Swarm Optimization (PSO) algorithm.
- To address limitations of current NAS methods by improving search efficiency and network performance.
Main Methods:
- Employed low-complexity residual architecture blocks as foundational units for diverse architecture exploration with minimal parameters.
- Implemented a depth initialization strategy to effectively confine the search space.
- Introduced novel particle difference computation and velocity update mechanisms to enhance trajectory exploration and particle diversity.
Main Results:
- The proposed method significantly improved search space utilization and particle diversity.
- Developed lightweight DCNNs with enhanced classification performance.
- Validated effectiveness on benchmark datasets and a custom 13-class crime dataset.
Conclusions:
- The enhanced PSO-based NAS strategy effectively designs lightweight DCNNs with superior classification accuracy.
- The approach overcomes key limitations of existing NAS methods, offering a more efficient and effective solution for DCNN architecture optimization.
