Offres de stage
Forum 'Stages' - Sujet créé le 2008-05-06
Internship Offer 1
This internship will be developed in the frame of the ILIAS group of the University of Luxembourg. It composed of two parts. The duration of each part is three months. They can be achieved by two different students or cumulated.
Part 1: Integration of SimGrid simulator with the JCell
Description
It consists in the integration of SimGrid simulator with the JCell framework. SimGrid is a grid simulator that allows the user to evaluate his/her scheduler. JCell is a framework for working mainly with Genetic Algorithms.
Objectives
The objectives of this part are:
1. Description of the design of SimGrid and the possibilities it offers in a document
2. Use one of the algorithms implemented in JCell (for example, a simple GA) as a scheduler for SimGrid.
3. Description of the integration of SimGrid with JCell in a document.
Part 2: Implementation in JCell of specialized heuristics for job scheduling
Description
This internship will be developed during three months in the frame of the ILIAS group of the University of Luxembourg. It consists in the implementation of heuristics in the JCell framework. JCell is a framework for working mainly with Genetic Algorithms.
Objectives
The objectives of this part are:
1. Implementation of the duplication-based state transition method, which is a local search method designed for metaheuristics.
2. Implementation of the methods Greedy Iterative Maximization (GIM), Minimum Completion Time (MCT), and the Sum Iterative Method (SIM) as local search methods or to be used for initializing the Genetic Algorithm population.
3. Description of the implementation and the results in a document.
Student(s) Skills
The following skills would be required for the student(s):
1. For part 1, some knowledge on Genetic Algorithm and Local Search Algorithms
2. For part 2, some knowledge on Grid and Parallel Computing and Evolutionary Algorithms would be desirable.
3. Java programming skills.
4. Speak English.
References
[1] JCell framework: https://jcell.gforge.uni.lu
Part 1:
[2] Y.C. Lee and A.Y. Zomaya, 2007, A novel state transition method for
metaheuristic-based scheduling in heterogeneous computing systems, IEEE Transactions on Parallel and Distributed Systems.
[3] P. Sugavanam, H.J. Siegel, A.A. Maciejewski, M. Oltikar, A. Mehta, R. Pichel, A. Horiuchi, V. Shestak, M. Al-Otaibi, Y. Krishnamurthy, S. Ali, J. Zhang, M.
Part 2:
[4] SimGrid simulator http://simgrid.gforge.inria.fr/
[5] Y.C. Lee and A.Y. Zomaya, 2007, Practical scheduling of bag of tasks applications on grids with dynamic resilience, IEEE Transactions on Computers, 56(6):815-825.
Internship Offer 2
Design and Implementation of PSO Algorithms in JCell
Description
This internship will be developed during six months in the frame of the ILIAS group of the University of Luxembourg. It consists in the implementation of a Particle Swarm Optimization (or PSO) algorithm in the JCell framework. The resulting PSO will be evaluated on a set of problems (both in the continuous and discrete domains) and it will be
compared against other Evolutionary Algorithms. Additionally, some design issues will be proposed, analyzed, and implemented with the goal of improving the behavior of the canonical PSO algorithm. Some examples are the application of cellular neighborhoods to the particles, or the self-adaptation of some of the parameters of the PSO algorithm, like the speed of particles.
Objectives
The objectives of this internship are:
1. Design and implementation of a Particle Swarm Optimization algorithm.
2. Validation of this algorithm versus other Evolutionary Algorithms.
3. Design and implementation of several techniques to improve the behavior of PSO.
4. Description of the algorithms designed and the results in a document.
Student Skills
The following skills would be required for the student:
1. Some knowledge on Evolutionary Algorithms, although it is not required to know
PSO algorithms.
2. Java programming skills.
3. Speak English.
References
[1] JCell framework: https://jcell.gforge.uni.lu
[2] Y. del Valle, G.K. Venayagamaoorthy, S. Mohagheghi, J.-C. Hernandez, and R.G. Harley, Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems, IEEE Transactions on Evolutionary Computation 12(2):171-195.
[3] J. Kennedy and R. Mendes, Neighborhood topologies in Fully Informed and Best-of-Neighborhood Particle Swarms, IEEE Transactions on Systems, Man, and Cybernetics- Part C: Applications and Reviews, 36(4):515-219
Contact:
Pascal Bouvry, office E013, pascal.bouvry@uni.lu
Bernabé Dorronsoro, office E001, bernabe.dorronsoro@uni.lu
Sadia Azem, office E008, sadia.azem@uni.lu
This internship will be developed in the frame of the ILIAS group of the University of Luxembourg. It composed of two parts. The duration of each part is three months. They can be achieved by two different students or cumulated.
Part 1: Integration of SimGrid simulator with the JCell
Description
It consists in the integration of SimGrid simulator with the JCell framework. SimGrid is a grid simulator that allows the user to evaluate his/her scheduler. JCell is a framework for working mainly with Genetic Algorithms.
Objectives
The objectives of this part are:
1. Description of the design of SimGrid and the possibilities it offers in a document
2. Use one of the algorithms implemented in JCell (for example, a simple GA) as a scheduler for SimGrid.
3. Description of the integration of SimGrid with JCell in a document.
Part 2: Implementation in JCell of specialized heuristics for job scheduling
Description
This internship will be developed during three months in the frame of the ILIAS group of the University of Luxembourg. It consists in the implementation of heuristics in the JCell framework. JCell is a framework for working mainly with Genetic Algorithms.
Objectives
The objectives of this part are:
1. Implementation of the duplication-based state transition method, which is a local search method designed for metaheuristics.
2. Implementation of the methods Greedy Iterative Maximization (GIM), Minimum Completion Time (MCT), and the Sum Iterative Method (SIM) as local search methods or to be used for initializing the Genetic Algorithm population.
3. Description of the implementation and the results in a document.
Student(s) Skills
The following skills would be required for the student(s):
1. For part 1, some knowledge on Genetic Algorithm and Local Search Algorithms
2. For part 2, some knowledge on Grid and Parallel Computing and Evolutionary Algorithms would be desirable.
3. Java programming skills.
4. Speak English.
References
[1] JCell framework: https://jcell.gforge.uni.lu
Part 1:
[2] Y.C. Lee and A.Y. Zomaya, 2007, A novel state transition method for
metaheuristic-based scheduling in heterogeneous computing systems, IEEE Transactions on Parallel and Distributed Systems.
[3] P. Sugavanam, H.J. Siegel, A.A. Maciejewski, M. Oltikar, A. Mehta, R. Pichel, A. Horiuchi, V. Shestak, M. Al-Otaibi, Y. Krishnamurthy, S. Ali, J. Zhang, M.
Part 2:
[4] SimGrid simulator http://simgrid.gforge.inria.fr/
[5] Y.C. Lee and A.Y. Zomaya, 2007, Practical scheduling of bag of tasks applications on grids with dynamic resilience, IEEE Transactions on Computers, 56(6):815-825.
Internship Offer 2
Design and Implementation of PSO Algorithms in JCell
Description
This internship will be developed during six months in the frame of the ILIAS group of the University of Luxembourg. It consists in the implementation of a Particle Swarm Optimization (or PSO) algorithm in the JCell framework. The resulting PSO will be evaluated on a set of problems (both in the continuous and discrete domains) and it will be
compared against other Evolutionary Algorithms. Additionally, some design issues will be proposed, analyzed, and implemented with the goal of improving the behavior of the canonical PSO algorithm. Some examples are the application of cellular neighborhoods to the particles, or the self-adaptation of some of the parameters of the PSO algorithm, like the speed of particles.
Objectives
The objectives of this internship are:
1. Design and implementation of a Particle Swarm Optimization algorithm.
2. Validation of this algorithm versus other Evolutionary Algorithms.
3. Design and implementation of several techniques to improve the behavior of PSO.
4. Description of the algorithms designed and the results in a document.
Student Skills
The following skills would be required for the student:
1. Some knowledge on Evolutionary Algorithms, although it is not required to know
PSO algorithms.
2. Java programming skills.
3. Speak English.
References
[1] JCell framework: https://jcell.gforge.uni.lu
[2] Y. del Valle, G.K. Venayagamaoorthy, S. Mohagheghi, J.-C. Hernandez, and R.G. Harley, Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems, IEEE Transactions on Evolutionary Computation 12(2):171-195.
[3] J. Kennedy and R. Mendes, Neighborhood topologies in Fully Informed and Best-of-Neighborhood Particle Swarms, IEEE Transactions on Systems, Man, and Cybernetics- Part C: Applications and Reviews, 36(4):515-219
Contact:
Pascal Bouvry, office E013, pascal.bouvry@uni.lu
Bernabé Dorronsoro, office E001, bernabe.dorronsoro@uni.lu
Sadia Azem, office E008, sadia.azem@uni.lu