Intensity-Modulated Radiation Therapy enhances dose delivery by dynamically adjusting beam intensities to target tumorous tissues while preserving healthy organs. One of the most effective planning approaches uses the Generalized Equivalent Uniform Dose metric, which ensures high-quality treatment plans but requires tuning several hyperparameters for each anatomical structure. Traditionally, this process is performed manually by clinical experts, making it time-consuming and dependent on human expertise. To address these challenges, a previous method combined multi-objective evolutionary search with gradient-based optimization to automate the tuning process. However, this hybrid strategy incurs high computational cost, as each candidate solution must undergo a complete gradient-based optimization step, repeated thousands of times throughout the process. This study introduces two complementary strategies to improve the efficiency of this framework. First, we analyze alternative multi-objective evolutionary algorithms that converge more rapidly, thereby reducing the number of required function evaluations, and we compare three gradient-based optimization methods to identify the one that accelerates convergence without compromising plan quality. Second, we implement a parallel computing framework that distributes the function evaluations across heterogeneous multicore computing clusters using a static batch scheduling strategy adapted to each node’s computational capacity. Combined, these algorithmic and computational enhancements yield an acceleration factor of 4049 compared to the original implementation. As a result, high-quality radiotherapy treatment plans can be automatically generated in approximately one hour, making this approach viable for integration into time-constrained clinical workflows.

ABSTRACT Intensity Modulated Radiotherapy (IMRT) employs radiation beams with varying angles and intensities to precisely target cancerous tissues while sparing healthy organs. Planning methods based on the generalized Equivalent Uniform Dose (gEUD) metric achieve excellent Planning Target Volume coverage. However, computing these plans requires extensive parameter adjustments and multiple model evaluations, making the process resource‐intensive and time‐consuming. This study aims to enhance the computational efficiency of radiotherapy plans by automating the adjustment of gEUD parameters, reducing solution times, and facilitating clinical integration. We introduced a novel approach that combines Gradient Descent algorithms with evolutionary optimization to explore the gEUD parameter space. This hybrid methodology generates radiation plans that meet clinical constraints. To address the high computational costs, we implemented parallelization and batching strategies, leveraging multicore servers to accelerate the optimization process and enable real‐time clinical applications. Benchmarking was conducted on three multicore platforms with distinct micro‐architectures, testing various batch sizes and thread configurations. Using a dataset of three Head and Neck IMRT patients treated with nine beams, our approach demonstrated substantial computational speed‐ups. Results confirmed the ability of the method to consistently produce high‐quality radiation therapy plans that meet clinical constraints. By effectively exploiting multicore servers, this approach overcomes the computational challenges of gEUD parameter tuning, enabling its integration into clinical practice. This advancement reduces planning times, supports medical physicists, and ultimately enhances patient care in radiotherapy.