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teaching:mfe:ia [2017/04/20 18:29] stuetzle [Automatic configuration of hybrid algorithms] |
teaching:mfe:ia [2017/04/20 18:44] stuetzle [Software framework for ant colony optimization] |
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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)]] | ||