United equilibrium optimizer for solving multimodal image registration

摘要

This study presents an optimization algorithm, called united equilibrium optimizer (UEO), which is modified from the equilibrium optimizer (EO). We improved the search structure of the EO and adjusted it using dynamic parameters. These improvements increase the potential of UEO in both exploration and exploitation, which makes UEO perform better in local minima avoidance and fast convergence. In this study, the UEO and other 11 algorithms were benchmarked with 30 unimodal, multimodal, and composition functions, as well as in medical image registration problems. In medical image registration problems, UEO is compared with the other three algorithms that have been successfully applied to medical image registration. The UEO has been tested for benchmark datasets, including three types of different modality images, from up to 16 patients, resulting in 41 multimodal registration scenarios. All the results demonstrated that UEO outperforms other algorithms in most cases, either in optimization tests or in practical problems. The source code of the UEO is publicly available at https://github.com/PengGui-N/United-Equilibrium-Optimizer.