Alejandro Santiago

Alejandro Santiago
Autonomous University of Tamaulipas | UAT · Faculty of Engineering "Arturo Narro Siller"

Doctor of Computer Science

About

28
Publications
6,529
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162
Citations
Introduction
Alejandro Santiago is a Full Professor at the Faculty of Engineering "Arturo Narro Siller" at the Autonomous University of Tamailipas, Tampico, Mexico.
Additional affiliations
January 2018 - August 2020
Polytechnic University of Altamira
Position
  • Professor

Publications

Publications (28)
Article
Full-text available
We propose a new accurate Micro Genetic Algorithm ( GA) for multi-objective optimization problems that we call Micro-FAME (or FAME). The distinctive feature of FAME with respect to the other existing multi-objective algorithms in the literature is its high elitism and fast convergence, produced by the application of the evolution directly on the...
Article
Full-text available
We propose a new method for multi-objective optimization, called Fuzzy Adaptive Multi-objective Evolutionary algorithm (FAME). It makes use of a smart operator controller that dynamically chooses the most promising variation operator to apply in the different stages of the search. This choice is guided by a fuzzy logic engine, according to the cont...
Article
Full-text available
High-Performance Computing systems rely on the software's capability to be highly parallelized in individual computing tasks. However, even with a high parallelization level, poor scheduling can lead to long runtimes; this scheduling is in itself an NP-hard problem. Therefore, it is our interest to use a heuristic approach, particularly Cellular Pr...
Article
Full-text available
The use of parallel applications in High-Performance Computing (HPC) demands high computing times and energy resources. Inadequate scheduling produces longer computing times which, in turn, increases energy consumption and monetary cost. Task scheduling is an NP-Hard problem; thus, several heuristics methods appear in the literature. The main appro...
Chapter
Full-text available
The multi-objective optimization methods are traditionally based on Pareto dominance or relaxed forms of dominance in order to achieve a representation of the Pareto front. However, the performance of traditional optimization methods decreases for those problems with more than three objectives to optimize. The decomposition of a multi-objective pro...
Article
Full-text available
In this work, we investigate the variant of the Internet Shopping Optimization Problem (ISHOP) that considers different item units. This variant is more challenging than the original problem. The original ISHOP is already known as a combinatorial NP-hard problem. In this work, we present a formal proof that the ISHOP variant considering different i...
Article
Full-text available
Thenumberofresearchpapersinterestedinstudyingthesocialdimensionofsupplychain sustainability and resilience is increasing in the literature. However, the social dimension is complex, with several uncertainty variables that cannot be expressed with a traditional Boolean logic of totally true or false. To cope with uncertainty, Fuzzy Logic allows the...
Article
Full-text available
This paper tackles the problem of forecasting real-life crime. However, the recollected data only produced thirty-five short-sized crime time series for three urban areas. We present a comparative analysis of four simple and four machine-learning-based ensemble forecasting methods. Additionally, we propose five forecasting techniques that manage th...
Article
Full-text available
This research addresses the two-dimensional strip packing problem to minimize the total strip height used, avoiding overlapping and placing objects outside the strip limits. This is an NP-hard optimization problem. We propose a greedy randomized adaptive search procedure (GRASP), incorporating flags as a new approach for this problem. These flags i...
Chapter
In this chapter is presented a comparative study of the performance of ten state-of-the-art multiobjective algorithms. The performance metrics used are hypervolume (HV), epsilon indicator (\( {\text{I}}_{{{{\varepsilon }} + }} \)), inverted generational distance (IGD), and generalized spread (\( {{\Delta }}^{ *} \)). The used instances belong to th...
Article
Full-text available
Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and...
Chapter
Full-text available
The problems of the real world, within which the variable time is present, have involved continuous changes. These problems usually change over time in their objectives, constraints or parameters. Therefore, it is necessary to carry out a readjustment when calculating their solution. This paper proposes an original way of approaching the project po...
Article
Full-text available
Path-metaheuristics have been used successfully in combinatorial optimization. However, in continuous optimization problems, the lack of neighborhood definitions makes them difficult to design and implement. This paper proposes a neighborhood operator based on first order linear approximation of the gradient. In order to adapt the linear approximat...
Conference Paper
Full-text available
Muchos de los algoritmos de optimización multi-objetivo más populares son poco eficaces al tratar con problemas de tres o más objetivos. Esto se debe en general al uso de estimadores de densidad, como la distancia de crowding de NSGA-II, que fueron diseñados cuando el principal reto era optimizar problemas de dos objetivos. En este artículo present...
Article
Full-text available
In this paper the Pareto optimization of the Heterogeneous Computing Scheduling Multi-Objective Problem (HCSMOP) is approached. The goal is to minimize two objectives which are in conflict: the overall completion time (makespan) and the energy consumed. In the revised literature, there are no reported exact algorithms which solve the HCSMOP. In thi...
Conference Paper
Full-text available
Este trabajo se enfoca en el problema de asignación de tareas independientes en sistemas de cómputo heterogéneo. La principal contribución es un estudio comparativo de diversas cruzas y mutaciones para el problema de consumo de energía para tareas sin precedencias en clústeres heterogéneos. Dentro de este comparativo se propone una mutación que apr...
Chapter
Full-text available
This chapter is focused on the problem of scheduling independent tasks on heterogeneous machines. The main contributions of our work are the following: a linear programming model to compute energy consumption for the execution of independent tasks on heterogeneous clusters, a constructive heuristic based on local search, and a new benchmark set. To...
Conference Paper
Full-text available
The paper deals with the problem of scheduling precedence-constrained applications on a distributed heterogeneous computing system with the aim of minimizing the response time or total execution time. The main contribution is a scheduling algorithm that promotes an iterative local search process. Due to a lack of generally accepted standard benchma...
Thesis
Full-text available
Multi-objective Precedence-constraint tasks scheduling on heterogeneous systems, Master's Thesis in Computer Science.
Conference Paper
Full-text available
Resumen En este artículo se aborda el problema de calendarización de tareas con precedencia en sistemas de procesamiento heterogéneo con el objetivo de minimizar su tiempo de ejecución. Se proponen dos algoritmos exactos basados en métodos bien conocidos: el enumerativo y el Branch and Bound, haciendo una comparación experimental en tiempos de ejec...
Conference Paper
Full-text available
We investigate the problem of scheduling precedence constrained applications on a distributed heterogeneous computing system with the aim of minimizing schedule length and reducing energy consumption. We present a scheduling algorithm based on the best-effort idea that promotes local search algorithms and dynamic voltage scaling to reduce energy co...
Article
Full-text available
In this paper a GRASP algorithm hybridized with a composite local search and path-relinking is proposed to solve the linear ordering problem with cumulative costs. Our approach consists on adding a composite local search that helps to produce diverse good solutions and improve them trough a truncated path-relinking with local search. The computatio...
Technical Report
Full-text available
Dynamic programming for the Cutwidth problem

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Projects

Projects (4)
Archived project
- Solving the Heterogeneous Computing Scheduling Problem with independent tasks and DVFS. - Approaching with exact methods and metaheuristics. - Experiment with new techniques.
Project
Development of metaheuristics for multi-objective optimization.