Examinando por Autor "Lozano Osorio, Isaac"

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A quick GRASP-based method for influence maximization in social networks
(Springer Nature, 2021-09-30) Lozano Osorio, Isaac; Sánchez-Oro Calvo, Jesús; Duarte Muñoz, Abraham; Cordón, Óscar
The evolution and spread of social networks have attracted the interest of the scientific community in the last few years. Specifically, several new interesting problems, which are hard to solve, have arisen in the context of viral marketing, disease analysis, and influence analysis, among others. Companies and researchers try to find the elements that maximize profit, stop pandemics, etc. This family of problems is collected under the term Social Network Influence Maximization problem (SNIMP), whose goal is to find the most influential users (commonly known as seeds) in a social network, simulating an influence diffusion model. SNIMP is known to be an NP-hard problem and, therefore, an exact algorithm is not suitable for solving it optimally in reasonable computing time. The main drawback of this optimization problem lies on the computational effort required to evaluate a solution. Since each node is infected with a certain probability, the objective function value must be calculated through a Monte Carlo simulation, resulting in a computationally complex process. The current proposal tries to overcome this limitation by considering a metaheuristic algorithm based on the Greedy Randomized Adaptive Search Procedure (GRASP) framework to design a quick solution procedure for the SNIMP. Our method consists of two distinct stages: construction and local search. The former is based on static features of the network, which notably increases its efficiency since it does not require to perform any simulation during construction. The latter involves a local search based on an intelligent neighborhood exploration strategy to find the most influential users based on swap moves, also aiming for an efficient processing. Experiments performed on 7 well-known social network datasets with 5 different seed set sizes confirm that the proposed algorithm is able to provide competitive results in terms of quality and computing time when comparing it with the best algorithms found in the state of the art.
A reactive path relinking algorithm for solving the bi-objective p-Median and p-Dispersion problem
(Springer, 2023) Lozano Osorio, Isaac; Sánchez-Oro, Jesús; López Sánchez, Ana Dolores; Duarte, Abraham
This paper deals with an interesting facility location problem known as the bi-objective p-Median and p-Dispersion problem (BpMD problem). The BpMD problem seeks to locate p facilities to service a set of n demand points, and the goal is to minimize the total distance between facilities and demand points and, simultaneously, maximize the minimum distance between all pairs of hosted facilities. The problem is addressed with a novel path relinking approach, called reactive path relinking, which hybridizes two of the most extended path relinking variants: interior path relinking and exterior path relinking. Additionally, the proposal is adapted to a multi-objective perspective for finding a good approximation of the Pareto front. Computational results prove the superiority of the proposed algorithm over the best procedures found in the literature.
Design and Implementation of Metaheuristic Algorithms for Social Network Influence Problems
(Universidad Rey Juan Carlos, 2024) Lozano Osorio, Isaac
Optimization has been a constant concern throughout history, from ancient Greeks seeking the most efficient way to organize cities to modern algorithms optimizing business processes. The significance of optimization lies in its ability to solve complex problems, enhance efficiency, and make informed decisions. Over centuries, optimization has proven to be fundamental for human progress. Nowadays, optimization has gained even greater importance across various fields, owing to the increasing complexity of the challenges we face. From business logistics to route planning in navigation, optimization has become an essential tool for tackling ever-evolving issues. The ability to efficiently and accurately solve problems is crucial in an increasingly interconnected world that heavily relies on technology. To address these challenges, there are various methodologies in optimization, including exact methods, approximations, genetic, and heuristic algorithms. These approaches offer flexible and adaptive solutions for a variety of problems, enabling researchers and professionals to find the best possible solution in different contexts. This thesis focuses specifically on problems related to Social Network Analysis, an area of study that has gained prominence in the digital age. Within this discipline, various problems are identified, with particular attention directed towards the concept of influence. The central problem involves selecting users within a social network in a way that maximizes or minimizes influence on other users, considering potential constraints such as maximum budgets. Defining influence within the context of social networks presents a significant challenge due to the diversity of available methods. The ability to strategically select users has practical applications in marketing campaigns, disease eradication, and the detection of misinformation campaigns. The complexity of these problems is exacerbated by the NP−hard nature of many of them, implying that finding exact solutions is impractical for large social networks. While approximate algorithms exist, in certain cases, it is crucial to have quick and high-quality information, such as in disease detection. Therefore, this thesis focuses on the use of heuristics and metaheuristics to address influence problems in social networks. These approaches provide efficient and adaptable solutions, particularly in situations where speed and precision are paramount. This thesis proposes different heuristic and metaheuristic algorithms to address the most widespread variants of influence problems in social networks. Various methodologies, such as Greedy Randomized Adaptive Procedure Search (GRASP) or Path Relinking (PR), have been applied and evaluated on real-world networks to verify their utility and applicability in these contexts. The results obtained surpass current proposals in all studied variants of social network influence problems.
Ejercicios y preguntas de la asignatura Metodologías de Desarrollo Seguro del Grado en Ingeniería de la Ciberseguridad
(2025) Lozano Osorio, Isaac; Martín Santamaría, Raúl
Ejercicios y preguntas de la asignatura Metodologías de Desarrollo Seguro, del Grado en Ingeniería en Ciberseguridad
Guía de estudio de la asignatura Metodologías de Desarrollo Seguro del Grado en Ingeniería de la Ciberseguridad
(2025) Lozano Osorio, Isaac; Martín Santamaría, Raúl
Guía de estudio de la asignatura Metodologías de Desarrollo Seguro, del Grado en Ingeniería en Ciberseguridad
Max-min dispersion with capacity and cost for a practical location problem
(Elsevier, 2022-03-26) Lozano Osorio, Isaac; Martínez Gavara, Anna; Martí, Rafael; Duarte, Abraham
Diversity and dispersion problems deal with selecting a subset of elements from a given set in such a way that their diversity is maximized. This study considers a practical location problem recently proposed in the context of max–min dispersion models. It is called the generalized dispersion problem, and it models realistic applications by introducing capacity and cost constraints. We propose two effective linear formulations for this problem, and develop a hybrid metaheuristic algorithm based on the variable neighborhood search methodology, to solve real instances. Extensive numerical computational experiments are performed to compare our hybrid metaheuristic with the state-of-art heuristic, and with integer linear programming formulations (ILP). Results on public benchmark instances show the superiority of our proposal with respect to the previous algorithms. Our extensive experimentation reveals that ILP models are able to optimally solve medium-size instances with the Gurobi optimizer, although metaheuristics outperform ILP both in running time and quality in large-size instances.
Optimizing Computer Networks Communication with the Band Collocation Problem: A Variable Neighborhood Search Approach
(MDPI, 2020-11-05) Lozano Osorio, Isaac; Sánchez-Oro Calvo, Jesús; Rodríguez García, Miguel Ángel; Duarte, Abraham
The Band Collocation Problem appears in the context of problems for optimizing telecommunication networks with the aim of solving some concerns related to the original Bandpass Problem and to present a more realistic approximation to be solved. This problem is interesting to optimize the cost of networks with several devices connected, such as networks with several embedded systems transmitting information among them. Despite the real-world applications of this problem, it has been mostly ignored from a heuristic point of view, with the Simulated Annealing algorithm being the best method found in the literature. In this work, three Variable Neighborhood Search (VNS) variants are presented, as well as three neighborhood structures and a novel optimization based on Least Recently Used cache, which allows the algorithm to perform an efficient evaluation of the objective function. The extensive experimental results section shows the superiority of the proposal with respect to the best previous method found in the state-of-the-art, emerging VNS as the most competitive method to deal with the Band Collocation Problem.
Otros materiales de la asignatura Metodologías de Desarrollo Seguro del Grado en Ingeniería de la Ciberseguridad
(2025) Lozano Osorio, Isaac; Martín Santamaría, Raúl
Otros materiales de la asignatura Metodologías de Desarrollo Seguro, del Grado en Ingeniería en Ciberseguridad
Prácticas de la asignatura Metodologías de Desarrollo Seguro del Grado en Ingeniería de la Ciberseguridad
(2025) Lozano Osorio, Isaac; Martín Santamaría, Raúl
Prácticas de la asignatura Metodologías de Desarrollo Seguro, del Grado en Ingeniería en Ciberseguridad
Software Ordenador de la asignatura Metodologías de Desarrollo Seguro del Grado en Ingeniería de la Ciberseguridad
(2025) Lozano Osorio, Isaac; Martín Santamaría, Raúl
Software de Ordenador de la asignatura Metodologías de Desarrollo Seguro, del Grado en Ingeniería en Ciberseguridad.
Transparencias de la asignatura Metodologías de Desarrollo Seguro del Grado en Ingeniería de la Ciberseguridad
(2025) Lozano Osorio, Isaac; Martín Santamaría, Raúl
Diapositivas de la asignatura Metodología de Desarrollo Seguro, del Grado en Ingeniería en Ciberseguridad de la Universidad Rey Juan Carlos. El temario es el siguiente: Tema 1. Buenas prácticas de desarrollo en los lenguajes de programación más habituales. Tema 2. Análisis estático de código. Tema 3. Análisis dinámico de código y otros mecanismos de test. Tema 4. Ciclos y metodologías de desarrollo seguro y su aplicación. Tema 5. Sistemas operativos de confianza.