Examinando por Autor "Oliva, Diego"

Mostrando 1 - 2 de 2
  • Miniatura
    Ítem
    A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem
    (Elsevier Ltd, 2024-10-15) Rodríguez Esparza, Erick; Masegosa Arredondo, Antonio David; Oliva, Diego; Onieva Caracuel, Enrique
    Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming due to the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASARL) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The implementation of the HH improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition.
  • Miniatura
    Ítem
    Optimizing road traffic surveillance: a robust hyper-heuristic approach for vehicle segmentation
    (Institute of Electrical and Electronics Engineers Inc., 2024) Rodríguez Esparza, Erick; Ramos Soto, Oscar; Masegosa Arredondo, Antonio David; Onieva Caracuel, Enrique; Oliva, Diego; Arriandiaga Laresgoiti, Ander; Ghosh, Arka
    Due to rising consumer demand and traffic congestion, last-mile logistics is becoming more challenging. To optimize urban distribution networks, digital image processing plays a key role in addressing these challenges through efficient traffic monitoring systems, an essential component of intelligent transportation systems. This paper introduces the Hyper-heuristic Genetic Algorithm based on Thompson Sampling with Diversity (HHGATSD), a novel approach to efficiently solving complex optimization and versatility problems in image segmentation. We evaluate its efficiency and robustness using the IEEE CEC2017 benchmark function set in general optimization problems with 30 and 50 dimensions. HHGATSD's applicability extends beyond optimization to computer vision in traffic management. First, the multilevel thresholding segmentation is performed on images extracted from the Berkeley Segmentation Dataset with minimum cross-entropy as the objective function, and its performance is compared using PSNR, SSIM, and FSIM metrics. Following that, the proposed methodology addresses the task of vehicle segmentation in traffic camera videos, reaffirming HHGATSD's effectiveness, adaptability, and consistency by consistently outperforming alternative segmentation methods found in the state-of-the-art. The results of comprehensive experiments, validated by statistical and non-parametric analyses, show that the proposed hyper-heuristic and methodology produce accurate and consistent segmentations for road traffic surveillance compared to the other methods in the literature