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Stochastic methods for combinatorial optimization


Often the combinatorial optimization problems are very hard and to find an optimal solution can take some months. If some does not necessary need un exact solution an approximation algorithm can be used instead. An approximation algorithms aims to find a " good" but not necessary the optimal one, within a realistic computational time. Stochastic algorithms are this kind of algorithms. A wide variety of optimization problems have been studied and can be found in the literature. Although they represent an impr essive diversity of real life decision problems. A procedure that is fast, easy and cheap to develop, understand, implement, execute and maintain and that consistently yield satisfactory solutions, is therefore often seen as the most suitable algorithm in practice. Examples of stochastic algorithms are simulated annealing, evolutionary computation, ant colony optimization, Monte Carlo methods. The problems on which these methods are applied come from real life and industry. The research method will be both practical and theoretical. It is structured along two orthogonal lines, stochastic methods and problems. The stochastic methods are one of the most effective methods to solve hard problems. The project will allow the applicant to reintegrate and to exchang e knowledge's. It will deep her knowledge's about stochastic methods learning different methodologies. It is not negligible that the period of re-integration will be followed by permanent position. The host institution is in the country of the nationality of the applicant and the project will help for the development of this less developed region of Europe.

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Acad. G. Bonchev Str. 25 A

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