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teaching:mfe:ia [2016/03/18 16:08] mdorigo Birattari's projects |
teaching:mfe:ia [2017/04/20 18:33] stuetzle |
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| - | ===== Software framework for Ant Colony Optimization ===== | + | ===== Software framework for ant colony optimization ===== |
| Ants have inspired a number of computational techniques and among the most successful is ant colony optimization (ACO). ACO is an optimization technique that can be applied to tackle a wide variety of computational problems that arise in computer science, telecommunications, and engineering. While ACO has a very wide applicability, the development times for effective ACO algorithms can be relatively high. This is due to the fact that each time a new problem is to be tackled by an ACO algorithm, a researcher needs to implement the algorithms almost from scratch. | Ants have inspired a number of computational techniques and among the most successful is ant colony optimization (ACO). ACO is an optimization technique that can be applied to tackle a wide variety of computational problems that arise in computer science, telecommunications, and engineering. While ACO has a very wide applicability, the development times for effective ACO algorithms can be relatively high. This is due to the fact that each time a new problem is to be tackled by an ACO algorithm, a researcher needs to implement the algorithms almost from scratch. | ||
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| The application of this software framework will be tested on a number of optimization problems. | The application of this software framework will be tested on a number of optimization problems. | ||
| - | Required skills: The candidate should be well acquainted with programming in object oriented languages. | + | Required skills: The candidate should be well acquainted with programming in object oriented languages. |
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| + | * Contacts : | ||
| + | * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]] | ||
| + | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
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| + | ===== Software framework for ant colony optimization ===== | ||
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| + | Ants have inspired a number of computational techniques and among the most successful is ant colony optimization (ACO). ACO is an optimization technique that can be applied to tackle a wide variety of computational problems that arise in computer science, telecommunications, and engineering. While ACO has a very wide applicability, the development times for effective ACO algorithms can be relatively high. This is due to the fact that each time a new problem is to be tackled by an ACO algorithm, a researcher needs to implement the algorithms almost from scratch. | ||
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| + | The goal of the project is to provide a software framework to support the application and the implementation of ACO algorithms to new problems. The software framework will offer all the standard procedures that are used in ACO algorithms and will allow for the rapid prototyping of ACO algorithms. | ||
| + | The application of this software framework will be tested on a number of optimization problems. | ||
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| + | Required skills: The candidate should be well acquainted with programming in object oriented languages. | ||
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| - | ===== Applications of a hybrid SLS algorithm framework ===== | + | ===== Automated configuration of hybrid algorithms ===== |
| - | We have recently developed a software framework from which hybrid stochastic local search algorithms can be designed automatically. This framework has only been applied to a few problems. The goal of this project would be to extend this framework to other problems and compare its results with the methods proposed in the literature. The student will learn to solve combinatorial optimization problems with SLS algorithms, automatic configuration of optimization algorithms, and analysis and comparison of optimization algorithms. | + | We have recently developed a software framework from which hybrid local search algorithms can be designed automatically. This framework has only been applied to a few problems. The goal of this project would be to extend this framework to other problems, in particular, vehicle routing problems and to compare the results that can be obtained with the methods proposed in the literature. The student will learn to solve combinatorial optimization problems with heuristic algorithms, automatic configuration of optimization algorithms, and the analysis and comparison of optimization algorithms. |
| * Contacts : | * Contacts : | ||