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teaching:mfe:ia [2016/03/18 16:08] mdorigo Birattari's projects |
teaching:mfe:ia [2018/03/23 11:45] stuetzle [MFE 2016-2017 : Intelligence Artificielle] |
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| - | ====== MFE 2015-2016 : Intelligence Artificielle ====== | + | ====== MFE 2017-2018 : Intelligence Artificielle ====== |
| ===== Introduction ===== | ===== Introduction ===== | ||
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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. |
| * Contacts : | * Contacts : | ||
| * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]] | ||
| + | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
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| + | ===== Automated configuration of multi-objective continuous optimizers ===== | ||
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| + | Many problems arising in real-world applications involve the optimization of various, often conflicting objectives. While the design of algorithms for tackling multi-objective problems has usually done manually, over the recent years automated design methodologies have been established and proved to be very powerful. | ||
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| + | The goal of this project is to extend the automated design to multi-objective continuous optimization problems. As the basis of the approach, a framework based on the two-phase plus Pareto local search approach will be developed into which basic search techniques for continuous optimization will be integrated. The goal is to build first a flexible framework from which then in a second step effective multi-objective optimizers will be generated exploiting automated algorithm design techniques. The final goal of this work is to participate in algorithm competitions with the goal of challenging the methodology. | ||
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| + | Required skills: The candidate should have very good analytical as well as programming skills. | ||
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| + | * Contacts : | ||
| * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
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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 : | ||