Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, a widely used approach is based on the Pareto front. In this document, a proposal is made for the analysis of the optimal front for multi-objective optimization problems using clustering techniques. With this approach, an alternative is sought for further use and improvement of multi-objective optimization algorithms considering solutions and clusters found. To carry out the clustering, the methods k-means and fuzzy c-means are employed, in such a way that there are two alternatives to generate the possible clusters. Regarding the results, it is observed that both clustering algorithms perform an adequate separation of the optimal Pareto continuous fronts; for discontinuous fronts, k-means and fuzzy c-means obtain results that complement each other (there is no superior algorithm). In terms of processing time, k-means presents less execution time than fuzzy c-means.

Currently, in Colombia, different problems in education exist; one of them is the inconvenience in tracing and controlling the learning trajectories that decide the topics taught in the country’s educational institutions. This work aims to implement a logic-based system that allows teachers and educational institutions to carry out a continuous monitoring process of students’ academic performance, facilitating early corrections of errors or failures in teaching methods, to promote educational support spaces within the educational institution.

As mentioned by the Food and Agriculture Organization of the United Nations, agriculture has a primary role in food security. Given the advantageous conditions that Colombia has as a biodiverse country, creating and implementing sustainable and comprehensive agricultural systems is essential to generate agricultural decision-making tools. Therefore, this paper displays the design and deployment (training–validation) of a neuro-fuzzy model for the relevant agricultural production in Colombia. Four different configurations are proposed according to the data collected and the variables identified. The results show that a remarkable prediction of the models (configurations) is achieved by using training and validation data.

Since cardiovascular diseases (CVDs) pose a critical global concern, identifying associated risk factors remains a pivotal research focus. This study aims to propose and optimize a fuzzy system for cardiovascular risk (CVR) classification using a multiobjective approach, addressing computational aspects such as the configuration of the fuzzy system, the optimization process, the selection of a suitable solution from the optimal Pareto front, and the interpretability of the fuzzy logic system after the optimization process. The proposed system utilizes data, including age, weight, height, gender, and systolic blood pressure to determine cardiovascular risk. The fuzzy model is based on preliminary information from the literature; therefore, to adjust the fuzzy logic system using a multiobjective approach, the body mass index (BMI) is considered as an additional output as data are available for this index, and body mass index is acknowledged as a proxy for cardiovascular risk given the propensity for these diseases attributed to surplus adipose tissue, which can elevate blood pressure, cholesterol, and triglyceride levels, leading to arterial and cardiac damage. By employing a multiobjective approach, the study aims to obtain a balance between the two outputs corresponding to cardiovascular risk classification and body mass index. For the multiobjective optimization, a set of experiments is proposed that render an optimal Pareto front, as a result, to later determine the appropriate solution. The results show an adequate optimization of the fuzzy logic system, allowing the interpretability of the fuzzy sets after carrying out the optimization process. In this way, this paper contributes to the advancement of the use of computational techniques in the medical domain.

This document seeks to model the behavior of drivers on urban roads considering different environmental factors using a Mamdani-type fuzzy system. For this, a leader-following traffic model and a fuzzy logic system are used to characterize the behavior of drivers. Real data are obtained using a camera in the roads under consideration, and these data and an optimization process are employed to fit the fuzzy model. For the optimization process, the fuzzy logic system used to model the driver’s behavior is incorporated into a dynamic vehicle tracking model where the fuzzy system allows considering different environmental factors in the traffic model simulation. After carrying out the optimization process, it is possible to assign linguistic labels to the fuzzy sets associated with the output. In this way, the interpretability of the proposed fuzzy system is achieved by assigning labels (concepts) to the fuzzy sets. The results show that the proposed model fits the real data, and the fuzzy sets are adjusted according to the measured data for the different considered cases.

Introducción: el artículo se deriva de la investigación “Caracterización de señales complejas con técnicas de Inteligencia Computacional” que se encuentra en curso desde el 2016 dentro del Grupo de Investigación Modelamiento en Ingeniería de Sistemas MIS de la Universidad Distrital Francisco José de Caldas. Objetivo: identificar el comportamiento caótico que presentan las ecuaciones de Lorenz empleando datos tomados directamente en el dominio del tiempo sin procesamiento previo, para lograr clasificar un sistema caótico. Metodología: en primer lugar, se obtienen los datos de entrenamiento mediante simulación. Posteriormente, se observa la respuesta del sistema utilizando datos de validación. Finalmente, se realiza una discusión y se establecen las conclusiones en función de los datos obtenidos. Resultados: para la mayoría de las máquinas de soporte vectorial utilizadas se observa una buena clasificación. Conclusión: para el conjunto de datos empleados se logró la clasificación del comportamiento caótico de las ecuaciones de Lorenz mediante máquinas de soporte vectorial, de tal forma que estas pueden ser una alternativa para realizar la clasificación de comportamientos, donde se toman datos directamente en el dominio del tiempo sin un procesamiento preliminar. Originalidad: esta investigación servirá de referente para posteriores desarrollos como el diagnóstico de pacientes utilizando señales biológicas. En este documento se busca observar las características que presentan las máquinas de soporte vectorial para la clasificación de un sistema caótico. Limitaciones: no se realiza un procesamiento preliminar de datos por lo cual la clasificación está sujeta por los valores obtenidos directamente de la simulación.