A Dual-Resolution Cooperative Evolutionary Algorithm for Multi-Objective IMRT Inverse Planning
Yuan Yao, Chen Li, Jun Yong Wu, Yi Zhang, Ying Song, Guangjun Li, Junjie Hu · 2025
Fluence map optimization problem (FMOP) refers to the optimization of the intensity of radiation beams, which is a crucial part of intensity-modulated radiation therapy (IMRT). The FMOP is often considered as a multi-objective optimization problem due to the numerous treatment objectives that need to be met. This paper formulates FMOP as an unconstrained two-objective optimization problem that focuses on dose-volume constraints and specifically designed a multi-objective coevolutionary optimization algorithm named MOEA/FMOP. The MOEA/FMOP utilizes a cooperative strategy to process dual populations with two different resolutions of fluence maps. One high-resolution population focuses on convergence and fine-tuning, while the other roughly encoded one serves to improve global convergence and maintain diversity. The resolutions of the populations are encoded and initialized according to the clinical methodology. In comparison to conventional MOEAs, MOEA/FMOP outperforms over five real-world cancer cases (including prostate, rectum, liver, nasopharynx and breast cases) in terms of hyper volume (HV) from the perspective of the performance indicator. Moreover, in the realm of clinical evaluation using dose-volume histograms (DVH), MOEA/FMOP exhibits a better capability to generate high-quality solutions concurrently.