Deep Learning-Tuned Adaptive Inertia Weight in Particle Swarm Optimization for Medical Image Registration

Katharina Krämer, Stefan Müller, Michael Kosterhon

2025

Abstract

A novel parameter training approach for Adaptive Inertia Weight Particle Swarm Optimization (AIW-PSO) using Deep Learning is proposed. In PSO, balancing exploration and exploitation is crucial, with inertia governing parameter space sampling. This work presents a method for training transfer function parameters that adjust the inertia weight based on a particle’s individual search ability (ISA) in each dimension. A neural network is used to train the parameters of this transfer function, which then maps the ISA value to a new inertia weight. During inference, the best possible success ratio and lowest average error are used as network inputs to predict optimal parameters. Interestingly, the parameters across different objective functions are similar and assume values that may appear spatially implausible, yet outperform all other considered value expressions. We evaluate the proposed method Deep Learning-Tuned Adaptive Inertia Weight (TAIW) against three inertia strategies: Constant Inertia Strategy (CIS), Linear Decreasing Inertia (LDI), Adaptive Inertia Weight (AIW) on three benchmark functions. Additionally, we apply these PSO inertia strategies to medical image registration, utilizing digitally reconstructed radiographs (DRRs). The results show promising improvements in alignment accuracy using TAIW. Finally, we introduce a metric that assesses search effectiveness based on multidimensional search space volumes.

Paper Citation

in Bibtex Style

@conference{visapp25,
author={Katharina Krämer and Stefan Müller and Michael Kosterhon},
title={Deep Learning-Tuned Adaptive Inertia Weight in Particle Swarm Optimization for Medical Image Registration},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={307-318},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013122000003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Deep Learning-Tuned Adaptive Inertia Weight in Particle Swarm Optimization for Medical Image Registration
SN - 978-989-758-728-3
AU - Krämer K.
AU - Müller S.
AU - Kosterhon M.
PY - 2025
SP - 307
EP - 318
DO - 10.5220/0013122000003912
PB - SciTePress

in Harvard Style

Krämer K., Müller S. and Kosterhon M. (2025). Deep Learning-Tuned Adaptive Inertia Weight in Particle Swarm Optimization for Medical Image Registration. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 307-318. DOI: 10.5220/0013122000003912