In this work, we focus on the problem of selecting low‐level heuristics in a hyperheuristic approach with offline learning, for the solution of instances of different problem domains. The objective is to improve the performance of the offline hyperheuristic approach, identifying equivalence classes in a set of instances of different problems and selecting the best performing heuristics in each of them. A methodology is proposed as the first step of a set of instances of all problems, and the generic characteristics of each instance and the performance of the heuristics in each one of them are considered to define the vectors of characteristics and make a grouping of classes. Metalearning with statistical tests is used to select the heuristics for each class. Finally, we used the Naive Bayes to test the set instances with k‐fold cross‐validation, and we compared all results statistically with the best‐known values. In this research, the methodology was tested by applying it to the problems of capacitated vehicle routing (CVRP) and graph coloring (GCP). The experimental results show that the proposed methodology can improve the performance of the offline hyperheuristic approach, correctly identifying the classes of instances and applying the appropriate heuristics in each case. This is based on the statistical comparison of the results obtained with those of the state of the art of each instance.

The rapid integration of electric vehicles (EVs) into transportation systems and power grids has significantly increased the complexity of optimization challenges related to routing, charging coordination, scheduling, and energy management. Despite significant research growth, the field remains conceptually fragmented, lacking a unified framework to systematically organize Electric Vehicle Optimization Problems (EVOPs). To address this gap, this study presents a systematic review of 144 peer-reviewed articles published between 2011 and January 2025 and proposes a structured EVOP taxonomy based on problem characteristics and dominant decision variables. The analysis examines mathematical formulations, solution methodologies, and emerging research trends. The results indicate the predominance of metaheuristic methods, while exact techniques are mainly limited to small-scale problems. Additionally, there is a growing trend toward multi-objective and stochastic models that incorporate uncertainty and dynamic decision-making environments. However, challenges remain regarding large-scale validation, standardized benchmarking, and integrated multi-domain modeling. The proposed taxonomy provides a coherent framework that facilitates comparison across optimization domains and supports the development of scalable and intelligent EV management systems.

En este trabajo se plantea un diseño de rutas para estaciones de carga eléctrica de vehículos eléctricos (VE) en el Estado de Guanajuato, utilizando algoritmos metaheurísticos. El crecimiento de la movilidad eléctrica plantea desafíos significativos en la planificación y optimización de infraestructuras de carga. En respuesta, este artículo propone una metodología basada en algoritmos metaheurísticos, específicamente algoritmos de búsqueda local iterada y la metodología de diseño propuesta por Ortiz-Aguilar et al (2015) [1], para optimizar la ubicación y distribución de estaciones de carga, maximizando la cobertura de servicio. Se presenta un estudio de caso detallado, utilizando datos geoespaciales de las estaciones de la República Mexicana, para validar la eficacia y viabilidad de la propuesta. Los resultados muestran que los enfoques metaheurísticos ofrecen soluciones competitivas en términos de cobertura de servicio, lo que demuestra su utilidad en la planificación de infraestructuras de carga para VE.