Aurelio Alejandro Santiago Pineda - Academia.edu (original) (raw)

Papers by Aurelio Alejandro Santiago Pineda

Supercomputing power is one of the fundamental pillars of the digital society, which depends on t... more Supercomputing power is one of the fundamental pillars of the digital society, which depends on the accurate scheduling of parallel applications in High-Performance Computing (HPC) centers to minimize computing times. However, precedence-constraint task scheduling is a well-known NP-Hard optimization problem, and no optimal polynomial-time algorithm exists to solve it. Therefore, as accurate as possible to the optimal values, heuristic algorithms are of relevant interest. Our new scheduling proposal name is EFT-GVNS, which stands for Earliest Finish Time-General Variable Neighborhood Search. EFT-GVNS uses a Composite Local Search (CLS), making our proposal more e cient than traditional GVNS. EFT-GVNS accuracy against four high-performance algorithms from the state-of-the-art (EDA, EFT-ILS, GRASP-CPA, MPQGA) and one reference algorithm in the literature (HEFT) is studied. Experimental results over four real-world applications (Fpppp, LIGO, Robot, Sparse) and 14 synthetic instances from the literature show that EFT-GVNS outperforms in terms of the median achieved results, with a global improvement of 37.6%, 27.4%, 17.8%, 6.1%, 2.2% to HEFT, EDA, EFT-ILS, GRASP-CPA, and MPQGA, respectively. EFT-GVNS achieves all the 14 optimal values of the synthetic benchmark.

Ediciones Universidad de Salamanca, Sep 15, 2016

Muchos de los algoritmos de optimizaci ́on multi-objetivo más populares son poco eficaces al trat... more Muchos de los algoritmos de optimizaci ́on multi-objetivo más populares son poco eficaces al tratar con problemas de tres o más obje- tivos. Esto se debe en general al uso de estimadores de densidad, como la distancia de crowding de NSGA-II, que fueron disen ̃ados cuando el principal reto era optimizar problemas de dos objetivos. En este artículo presentamos un nuevo estimador de densidad que permite a los algoritmos que utilizan archivos externos mejorar notablemente la diversidad de soluciones de los frentes al resolver problemas de tres objetivos. Hemos evaluado el estimador en dos variantes de algoritmos basados en archivo, SMPSO y MOCell, comparado los resultados obtenidos con los de las versiones originales de los mismos sobre un banco de pruebas compuesto por 16 problemas de tres objetivos. Los resultados muestran que los frentes obtenidos con nuestra propuesta mejoran de forma significativa a los que generan los algoritmos originales.

Este trabajo se enfoca en el problema de asignación de tareas independientes en sistemas de cómpu... more 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 aprovecha características del problema, la cual se denomina balanceo de carga. Para el control de la energía se usa la técnica de escalamiento dinámico de voltaje y frecuencia (DVFS, por sus siglas en inglés). La comparación se desarrolló utilizando el algoritmo multiobjetivo NSGA-II.

In this paper the Pareto optimization of the Heterogeneous Computing Scheduling Multi-Objective P... more 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 this work, we propose a Branch and Bound algorithm to solve the problem and it is used to find the optimal Pareto front of a set of instances of the literature. This set is the first available benchmark to assess the performance of multiobjective algorithms with quality metrics that requires known the optimal front of the instances.

Path-metaheuristics have been used successfully in combinatorial optimization. However, in contin... more 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 approximation to multi-objective optimization, we use the multi-objective decomposition approach so the operator can be used for single and multi-objective continuous optimization problems. The proposed approach is validated using a Threshold Accepting algorithm based on decomposition and a set of benchmark problems for multi-objective optimization. Results show a significant improvement over Pareto lineal sets.

Applied Sciences, 2020

High-Performance Computing systems rely on the software’s capability to be highly parallelized in... more 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 Processing Algorithms (CPA), which is a novel metaheuristic framework for optimization. This framework has its foundation in exploring the search space by multiple Processing Cells that communicate to exploit the search and in the individual stagnation detection mechanism in the Processing Cells. In this paper, we proposed using a Greedy Randomized Adaptive Search Procedure (GRASP) to look for promising task execution orders; later, a CPA formed with Iterated Local Search (ILS) Processing Cells is used for the optimization. We assess our approach with a high-performance ILS state-of-the-art approach. Experimental results show that the CPA ou...

Studies in Computational Intelligence, 2015

ABSTRACT This chapter is focused on the problem of scheduling independent tasks on heterogeneous ... more ABSTRACT 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 assess our approach we compare the performance of two solution methods: a memetic algorithm, based on population search and local search, and a seeded genetic algorithm, based on NSGA-II. A Wilcoxon rank-sum test shows significant differences in the diversity of solutions found but not in hypervolume. The memetic algorithm gets the best diversity for a bigger instance set from the state of the art.

Applied Sciences, 2022

This research addresses the two-dimensional strip packing problem to minimize the total strip hei... more 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 indicate available space after accommodating an object; they hold the available width and height for the following objects. We also propose three waste functions as surrogate objective functions for the GRASP candidate list and use and enhanced selection for the restricted candidate list, limiting the object options to better elements. Finally, we use overlapping functions to ensure that the object fits in the flag because there are some cases where a flag’s width can be wrong due to new object placement. The tests showed that our proposal outperforms the most recent state-of-the-art metaheuristic. Additionally, we make comparisons against ...

Symmetry

Electricity is one of the most important resources for the growth and sustainability of the popul... more 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 a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease e...

"The paper deals with the problem of scheduling precedence-constrained applications on a dis... more "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 benchmarks for the evaluation of scheduling algorithms in the heterogeneous computing systems we also generate a benchmark of synthetic instances. The benchmark is composed of small size synthetic deterministic non-preemptive program graphs known in the literature. We compute the optimal solution and the global optimal value with an exact enumerative search method that explores all possible solutions. We compare the performance of the proposed local search algorithm with the optimal values. We have simulated the proposed algorithm using graphs obtained from real-world applications emphasizing the interest of the approach."

Studies in Computational Intelligence, 2014

ABSTRACT The multi-objective optimization methods are traditionally based on Pareto dominance or ... more ABSTRACT 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 problem is an approach that transforms a multi-objective problem into many single-objective optimization problems, avoiding the need of any dominance form. This chapter provides a short review of the general framework, current research trends and future research topics on decomposition methods.

Multi-objective optimization density estimation evolutionary algorithm adaptive algorithm fuzzy l... more Multi-objective optimization density estimation evolutionary algorithm adaptive algorithm fuzzy logic a b s t r a c t 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 contributions of the different operators in the past. FAME also includes a novel effective density estimator with polynomial complexity, called Spatial Spread Deviation (SSD). Our proposal follows a steady-state selection scheme and includes an external archive implementing SSD to identify the candidate solutions to be removed when it becomes full. To assess the performance of our proposal, we compare FAME with a number of state of the art algorithms (MOEA/D-DE, SMEA , SMPSOhv, SMS-EMOA , and BORG) on a set of difficult problems. The results show that FAME achieves the best overall performance.

High-Performance Computing systems rely on the software's capability to be highly parallelized in... more 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 Processing Algorithms (CPA), which is a novel metaheuristic framework for optimization. This framework has its foundation in exploring the search space by multiple Processing Cells that communicate to exploit the search and in the individual stagnation detection mechanism in the Processing Cells. In this paper, we proposed using a Greedy Randomized Adaptive Search Procedure (GRASP) to look for promising task execution orders; later, a CPA formed with Iterated Local Search (ILS) Processing Cells is used for the optimization. We assess our approach with a high-performance ILS state-of-the-art approach. Experimental results show that the CPA outperforms the previous ILS in real applications and synthetic instances.

Memorias de residencia, 2011

Dynamic Programming for the cutwdith problem

Path-metaheuristics have been used successfully in combinatorial optimization. However, in contin... more 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 approximation to multi-objective optimization, we use the multi-objective decomposition approach so the operator can be used for single and multi-objective continuous optimization problems. The proposed approach is validated using a Threshold Accepting algorithm based on decomposition and a set of benchmark problems for multi-objective optimization. Results show a significant improvement over Pareto lineal sets.

In this paper a GRASP algorithm hybridized with a composite local search and path-relinking is pr... more 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 computational results show that the GRASP algorithm finds 30 new best known solutions of the one hundred twenty three standard instances used with unknown optimal values. Also it shows that the GRASP algorithm outperforms to the best reported solution (Tabu search), when a nonparametric Wilcoxon test is applied.

2012 International Conference on High Performance Computing & Simulation (HPCS), 2012

Abstract We investigate the problem of scheduling precedence constrained applications on a distri... more Abstract 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-...

The multi-objective optimization methods are traditionally based on Pareto dominance or relaxed f... more 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 problem is an approach that transforms a multi-objective problem into many single-objective optimization problems, avoiding the need of any dominance form. This chapter provides a short review of the general framework, current research trends and future research topics on decomposition methods.

Supercomputing power is one of the fundamental pillars of the digital society, which depends on t... more Supercomputing power is one of the fundamental pillars of the digital society, which depends on the accurate scheduling of parallel applications in High-Performance Computing (HPC) centers to minimize computing times. However, precedence-constraint task scheduling is a well-known NP-Hard optimization problem, and no optimal polynomial-time algorithm exists to solve it. Therefore, as accurate as possible to the optimal values, heuristic algorithms are of relevant interest. Our new scheduling proposal name is EFT-GVNS, which stands for Earliest Finish Time-General Variable Neighborhood Search. EFT-GVNS uses a Composite Local Search (CLS), making our proposal more e cient than traditional GVNS. EFT-GVNS accuracy against four high-performance algorithms from the state-of-the-art (EDA, EFT-ILS, GRASP-CPA, MPQGA) and one reference algorithm in the literature (HEFT) is studied. Experimental results over four real-world applications (Fpppp, LIGO, Robot, Sparse) and 14 synthetic instances from the literature show that EFT-GVNS outperforms in terms of the median achieved results, with a global improvement of 37.6%, 27.4%, 17.8%, 6.1%, 2.2% to HEFT, EDA, EFT-ILS, GRASP-CPA, and MPQGA, respectively. EFT-GVNS achieves all the 14 optimal values of the synthetic benchmark.

Ediciones Universidad de Salamanca, Sep 15, 2016

Muchos de los algoritmos de optimizaci ́on multi-objetivo más populares son poco eficaces al trat... more Muchos de los algoritmos de optimizaci ́on multi-objetivo más populares son poco eficaces al tratar con problemas de tres o más obje- tivos. Esto se debe en general al uso de estimadores de densidad, como la distancia de crowding de NSGA-II, que fueron disen ̃ados cuando el principal reto era optimizar problemas de dos objetivos. En este artículo presentamos un nuevo estimador de densidad que permite a los algoritmos que utilizan archivos externos mejorar notablemente la diversidad de soluciones de los frentes al resolver problemas de tres objetivos. Hemos evaluado el estimador en dos variantes de algoritmos basados en archivo, SMPSO y MOCell, comparado los resultados obtenidos con los de las versiones originales de los mismos sobre un banco de pruebas compuesto por 16 problemas de tres objetivos. Los resultados muestran que los frentes obtenidos con nuestra propuesta mejoran de forma significativa a los que generan los algoritmos originales.

Este trabajo se enfoca en el problema de asignación de tareas independientes en sistemas de cómpu... more 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 aprovecha características del problema, la cual se denomina balanceo de carga. Para el control de la energía se usa la técnica de escalamiento dinámico de voltaje y frecuencia (DVFS, por sus siglas en inglés). La comparación se desarrolló utilizando el algoritmo multiobjetivo NSGA-II.

In this paper the Pareto optimization of the Heterogeneous Computing Scheduling Multi-Objective P... more 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 this work, we propose a Branch and Bound algorithm to solve the problem and it is used to find the optimal Pareto front of a set of instances of the literature. This set is the first available benchmark to assess the performance of multiobjective algorithms with quality metrics that requires known the optimal front of the instances.

Path-metaheuristics have been used successfully in combinatorial optimization. However, in contin... more 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 approximation to multi-objective optimization, we use the multi-objective decomposition approach so the operator can be used for single and multi-objective continuous optimization problems. The proposed approach is validated using a Threshold Accepting algorithm based on decomposition and a set of benchmark problems for multi-objective optimization. Results show a significant improvement over Pareto lineal sets.

Applied Sciences, 2020

High-Performance Computing systems rely on the software’s capability to be highly parallelized in... more 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 Processing Algorithms (CPA), which is a novel metaheuristic framework for optimization. This framework has its foundation in exploring the search space by multiple Processing Cells that communicate to exploit the search and in the individual stagnation detection mechanism in the Processing Cells. In this paper, we proposed using a Greedy Randomized Adaptive Search Procedure (GRASP) to look for promising task execution orders; later, a CPA formed with Iterated Local Search (ILS) Processing Cells is used for the optimization. We assess our approach with a high-performance ILS state-of-the-art approach. Experimental results show that the CPA ou...

Studies in Computational Intelligence, 2015

ABSTRACT This chapter is focused on the problem of scheduling independent tasks on heterogeneous ... more ABSTRACT 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 assess our approach we compare the performance of two solution methods: a memetic algorithm, based on population search and local search, and a seeded genetic algorithm, based on NSGA-II. A Wilcoxon rank-sum test shows significant differences in the diversity of solutions found but not in hypervolume. The memetic algorithm gets the best diversity for a bigger instance set from the state of the art.

Applied Sciences, 2022

This research addresses the two-dimensional strip packing problem to minimize the total strip hei... more 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 indicate available space after accommodating an object; they hold the available width and height for the following objects. We also propose three waste functions as surrogate objective functions for the GRASP candidate list and use and enhanced selection for the restricted candidate list, limiting the object options to better elements. Finally, we use overlapping functions to ensure that the object fits in the flag because there are some cases where a flag’s width can be wrong due to new object placement. The tests showed that our proposal outperforms the most recent state-of-the-art metaheuristic. Additionally, we make comparisons against ...

Symmetry

Electricity is one of the most important resources for the growth and sustainability of the popul... more 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 a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease e...

"The paper deals with the problem of scheduling precedence-constrained applications on a dis... more "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 benchmarks for the evaluation of scheduling algorithms in the heterogeneous computing systems we also generate a benchmark of synthetic instances. The benchmark is composed of small size synthetic deterministic non-preemptive program graphs known in the literature. We compute the optimal solution and the global optimal value with an exact enumerative search method that explores all possible solutions. We compare the performance of the proposed local search algorithm with the optimal values. We have simulated the proposed algorithm using graphs obtained from real-world applications emphasizing the interest of the approach."

Studies in Computational Intelligence, 2014

ABSTRACT The multi-objective optimization methods are traditionally based on Pareto dominance or ... more ABSTRACT 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 problem is an approach that transforms a multi-objective problem into many single-objective optimization problems, avoiding the need of any dominance form. This chapter provides a short review of the general framework, current research trends and future research topics on decomposition methods.

Multi-objective optimization density estimation evolutionary algorithm adaptive algorithm fuzzy l... more Multi-objective optimization density estimation evolutionary algorithm adaptive algorithm fuzzy logic a b s t r a c t 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 contributions of the different operators in the past. FAME also includes a novel effective density estimator with polynomial complexity, called Spatial Spread Deviation (SSD). Our proposal follows a steady-state selection scheme and includes an external archive implementing SSD to identify the candidate solutions to be removed when it becomes full. To assess the performance of our proposal, we compare FAME with a number of state of the art algorithms (MOEA/D-DE, SMEA , SMPSOhv, SMS-EMOA , and BORG) on a set of difficult problems. The results show that FAME achieves the best overall performance.

High-Performance Computing systems rely on the software's capability to be highly parallelized in... more 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 Processing Algorithms (CPA), which is a novel metaheuristic framework for optimization. This framework has its foundation in exploring the search space by multiple Processing Cells that communicate to exploit the search and in the individual stagnation detection mechanism in the Processing Cells. In this paper, we proposed using a Greedy Randomized Adaptive Search Procedure (GRASP) to look for promising task execution orders; later, a CPA formed with Iterated Local Search (ILS) Processing Cells is used for the optimization. We assess our approach with a high-performance ILS state-of-the-art approach. Experimental results show that the CPA outperforms the previous ILS in real applications and synthetic instances.

Memorias de residencia, 2011

Dynamic Programming for the cutwdith problem

Path-metaheuristics have been used successfully in combinatorial optimization. However, in contin... more 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 approximation to multi-objective optimization, we use the multi-objective decomposition approach so the operator can be used for single and multi-objective continuous optimization problems. The proposed approach is validated using a Threshold Accepting algorithm based on decomposition and a set of benchmark problems for multi-objective optimization. Results show a significant improvement over Pareto lineal sets.

In this paper a GRASP algorithm hybridized with a composite local search and path-relinking is pr... more 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 computational results show that the GRASP algorithm finds 30 new best known solutions of the one hundred twenty three standard instances used with unknown optimal values. Also it shows that the GRASP algorithm outperforms to the best reported solution (Tabu search), when a nonparametric Wilcoxon test is applied.

2012 International Conference on High Performance Computing & Simulation (HPCS), 2012

Abstract We investigate the problem of scheduling precedence constrained applications on a distri... more Abstract 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-...

The multi-objective optimization methods are traditionally based on Pareto dominance or relaxed f... more 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 problem is an approach that transforms a multi-objective problem into many single-objective optimization problems, avoiding the need of any dominance form. This chapter provides a short review of the general framework, current research trends and future research topics on decomposition methods.

Multi-objective Precedence-constraint tasks scheduling on heterogeneous systems, Master's Thesis ... more Multi-objective Precedence-constraint tasks scheduling on heterogeneous systems, Master's Thesis in Computer Science.

Muchos de los algoritmos de optimizaci ́on multi-objetivo más populares son poco eficaces al trat... more Muchos de los algoritmos de optimizaci ́on multi-objetivo más populares son poco eficaces al tratar con problemas de tres o más obje- tivos. Esto se debe en general al uso de estimadores de densidad, como la distancia de crowding de NSGA-II, que fueron disen ̃ados cuando el principal reto era optimizar problemas de dos objetivos. En este artículo presentamos un nuevo estimador de densidad que permite a los algoritmos que utilizan archivos externos mejorar notablemente la diversidad de soluciones de los frentes al resolver problemas de tres objetivos. Hemos evaluado el estimador en dos variantes de algoritmos basados en archivo, SMPSO y MOCell, comparado los resultados obtenidos con los de las versiones originales de los mismos sobre un banco de pruebas compuesto por 16 problemas de tres objetivos. Los resultados muestran que los frentes obtenidos con nuestra propuesta mejoran de forma significativa a los que generan los algoritmos originales.

Resumen En este artículo se aborda el problema de calendarización de tareas con precedencia en si... more 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 ejecución.