ORIGINAL RESEARCH article
Front. Mech. Eng.
Sec. Mechatronics
Multi-objective optimization of beam transport in medical heavy ion accelerators using an improved non-dominated sorting differential evolution algorithm (NSDE)
Provisionally accepted
- 1Wuwei Vocational College, Wuwei, China
- 2Gansu Nuclear Geological 212nd Brigade, Gansu, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: To address the problem of high-dimensional coupling parameters easily getting trapped in local optima and multi-objective conflicts in beam transport of medical heavy ion accelerators, this paper proposes an improved NSDE(Non-Dominated Sort Differential Evolutionary) Algorithm. Three methodological improvements are introduced: reverse learning initialization to significantly enhance population diversity; an adaptive mechanism for online adjustment of mutation factors and crossover probabilities to balance exploration and exploitation; and PSO local enhancement based on crowding distance to refine the non-dominated elite solution. Main Results: Large-scale experiments based on FLUKA Monte Carlo coupling simulations (nine-dimensional decision variables) show that the improved NSDE increases beam transport efficiency from the baseline of 92.42% to 99.21% (an improvement of 6.79%), while also achieving consistent improvements in key physical indicators such as end-point spot size, system power consumption, and energy retention rate. Conclusion: The results demonstrate that the proposed method has significant advantages in improving optimization quality and maintaining robustness, and is suitable for accelerator engineering optimization with strict requirements for real-time performance and multi-objective accuracy.
Keywords: Adaptive mechanism, beam transmission efficiency, Medical Heavy Ion Accelerator, NSDE Algorithm, PSO algorithm
Received: 05 Nov 2025; Accepted: 17 Dec 2025.
Copyright: © 2025 Yang, Zhang and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yanhong Yang
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.