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Communication dans un congrès
Finding and proving the optimum : cooperative stochastic and deterministic search
Abstract : In this article, we introduce a global cooperative approach between an Interval Branch and Bound Algorithm and an Evolutionary Algorithm, that takes advantage of both methods to optimize a function for which an inclusion function can be expressed. The Branch and Bound algorithm deletes whole blocks of the search space whereas the Evolutionary Algorithm looks for the optimum in the remaining space and sends to the IBBA the best evaluation found in order to improve its Bound. The two algorithms run independently and update common information through shared memory. The cooperative algorithm prevents premature and local convergence of the evolutionary algorithm, while speeding up the convergence of the branch and bound algorithm. Moreover, the result found is the proved global optimum. In part 1, a short background is introduced. Part 2.1 describes the basic Interval Branch and Bound Algorithm and part 2.2 the Evolutionary Algorithm. Part 3 introduces the cooperative algorithm and part 4 gives the results of the algorithms on benchmark functions. The last part concludes and gives suggestions of avenues of further research.
Littérature citée [20 références]
https://hal-enac.archives-ouvertes.fr/hal-00938712
Contributeur : Laurence Porte Connectez-vous pour contacter le contributeur
Soumis le : jeudi 24 avril 2014 - 15:17:30
Dernière modification le : mardi 19 octobre 2021 - 14:24:12
Archivage à long terme le : : jeudi 24 juillet 2014 - 10:37:37
Contributeur : Laurence Porte Connectez-vous pour contacter le contributeur
Soumis le : jeudi 24 avril 2014 - 15:17:30
Dernière modification le : mardi 19 octobre 2021 - 14:24:12
Archivage à long terme le : : jeudi 24 juillet 2014 - 10:37:37
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