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.

In this study, a digital twin for a flat plate solar collector field is proposed. This kind of system is used to reduce carbon dioxide emissions in bioclimatic buildings to convert them into Zero Energy Buildings. The core of the digital twin is an Artificial Neural Network prediction model, which is a good alternative to models based on physical equations for modeling systems with strong non-linearities, such as the ones found in flat plate solar collectors. The Artificial Neural Network prediction model is calibrated and validated with data saved during one year of operation comprising sunny days, cloudy days, partially cloudy days and non-operation days. Validation shows good results using several statistical metrics, suggesting that the Artificial Neural Network model is suitable for operation and control purposes. With a highly accurate virtual representation, the Artificial Neural Network model allows data analysis of the plant operator, prediction of behavior, and offers recommendations for optimizing system performance. In addition, the digital twin presented as part of this work is not just limited to the model, but is also enriched by the integration of data acquisition technologies and a user interface into a web page. This innovative integration establishes a robust framework for proactive, real-time decision-making and efficient management of the plant, ensuring enhanced system operation and sustainability.

In this work, we introduce an energy management system (EMS) that focuses on minimizing two objective functions simultaneously, i.e. the cost of energy and the production of CO2in a microgrid. As a result, a set of equally valuable solutions are proposed. The decision-maker is then in charge of deciding and considering the solution that best fits his/her current preferences. If the condition changes, the user or operator can select another solution without solving the optimization problem again. To test the EMS, the microgrid located at the CIESOL building at the University of Almería has been considered.

A continuous location problem in which a firm wants to set up two or more new facilities in a competitive environment is considered. Other facilities offering the same product or service already exist in the area. Both the locations and the qualities of the new facilities are to be found so as to maximize the profit obtained by the firm. This is a global optimization problem, with many local optima. In this paper we analyze several approaches to solve it, namely, three multistart local search heuristics, a multistart simulated annealing algorithm, and two variants of an evolutionary algorithm. Through a comprehensive computational study it is shown that the evolutionary algorithms are the heuristics that provide the best solutions. Furthermore, using a set of problems for which the optimal solutions are known, only the evolutionary algorithms were able to find the optimal solutions for all the instances. The evolutionary strategies presented in this paper can be easily adapted to handle other continuous location problems.